{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: If you miss a compact list, please try `print_header`!\n",
      "The `sinfo` package has changed name and is now called `session_info` to become more discoverable and self-explanatory. The `sinfo` PyPI package will be kept around to avoid breaking old installs and you can downgrade to 0.3.2 if you want to use it without seeing this message. For the latest features and bug fixes, please install `session_info` instead. The usage and defaults also changed slightly, so please review the latest README at https://gitlab.com/joelostblom/session_info.\n",
      "-----\n",
      "anndata     0.7.6\n",
      "scanpy      1.8.1\n",
      "sinfo       0.3.4\n",
      "-----\n",
      "MulticoreTSNE       NA\n",
      "PIL                 8.0.1\n",
      "attr                20.3.0\n",
      "backcall            0.2.0\n",
      "bottleneck          1.3.2\n",
      "cffi                1.14.3\n",
      "cloudpickle         1.6.0\n",
      "colorama            0.4.4\n",
      "cycler              0.10.0\n",
      "cython_runtime      NA\n",
      "cytoolz             0.11.0\n",
      "dask                2.30.0\n",
      "dateutil            2.8.1\n",
      "decorator           4.4.2\n",
      "fa2                 NA\n",
      "fcsparser           0.2.2\n",
      "h5py                2.10.0\n",
      "harmony             0.1.4\n",
      "idna                2.10\n",
      "igraph              0.9.6\n",
      "ipykernel           5.3.4\n",
      "ipython_genutils    0.2.0\n",
      "ipywidgets          7.5.1\n",
      "jedi                0.17.1\n",
      "jinja2              2.11.2\n",
      "joblib              0.17.0\n",
      "jsonschema          3.2.0\n",
      "kiwisolver          1.3.0\n",
      "leidenalg           0.8.7\n",
      "llvmlite            0.34.0\n",
      "lxml                4.6.1\n",
      "markupsafe          1.1.1\n",
      "matplotlib          3.3.2\n",
      "mkl                 2.3.0\n",
      "mpl_toolkits        NA\n",
      "natsort             7.1.1\n",
      "nbformat            5.0.8\n",
      "networkx            2.5\n",
      "nt                  NA\n",
      "ntsecuritycon       NA\n",
      "numba               0.51.2\n",
      "numexpr             2.7.1\n",
      "numpy               1.19.2\n",
      "packaging           20.4\n",
      "palantir            1.0.0\n",
      "pandas              1.1.3\n",
      "parso               0.7.0\n",
      "phenograph          1.5.7\n",
      "pickleshare         0.7.5\n",
      "pkg_resources       NA\n",
      "prometheus_client   NA\n",
      "prompt_toolkit      3.0.8\n",
      "psutil              5.7.2\n",
      "pvectorc            NA\n",
      "pycparser           2.20\n",
      "pygments            2.7.2\n",
      "pynndescent         0.5.4\n",
      "pyparsing           2.4.7\n",
      "pyrsistent          NA\n",
      "pythoncom           NA\n",
      "pytz                2020.1\n",
      "pywintypes          NA\n",
      "scipy               1.5.2\n",
      "seaborn             0.11.0\n",
      "send2trash          NA\n",
      "six                 1.15.0\n",
      "sklearn             0.23.2\n",
      "sphinxcontrib       NA\n",
      "statsmodels         0.12.0\n",
      "storemagic          NA\n",
      "tables              3.6.1\n",
      "tblib               1.7.0\n",
      "terminado           0.9.1\n",
      "texttable           1.6.4\n",
      "tlz                 0.11.0\n",
      "toolz               0.11.1\n",
      "tornado             6.0.4\n",
      "tqdm                4.50.2\n",
      "traitlets           5.0.5\n",
      "typing_extensions   NA\n",
      "umap                0.5.1\n",
      "wcwidth             0.2.5\n",
      "win32api            NA\n",
      "win32com            NA\n",
      "win32security       NA\n",
      "winpty              0.5.7\n",
      "yaml                5.3.1\n",
      "zmq                 19.0.2\n",
      "zope                NA\n",
      "-----\n",
      "IPython             7.19.0\n",
      "jupyter_client      6.1.7\n",
      "jupyter_core        4.6.3\n",
      "jupyterlab          2.2.6\n",
      "notebook            6.1.4\n",
      "-----\n",
      "Python 3.8.5 (default, Sep  3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)]\n",
      "Windows-10-10.0.19041-SP0\n",
      "24 logical CPU cores, Intel64 Family 6 Model 85 Stepping 7, GenuineIntel\n",
      "-----\n",
      "Session information updated at 2021-09-14 14:15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import scanpy.external as sce\n",
    "sc.logging.print_versions()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "import palantir\n",
    "import tkinter\n",
    "import harmony\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HES4</th>\n",
       "      <th>ISG15</th>\n",
       "      <th>AGRN</th>\n",
       "      <th>TNFRSF18</th>\n",
       "      <th>TNFRSF4</th>\n",
       "      <th>MXRA8</th>\n",
       "      <th>MMP23B</th>\n",
       "      <th>CDK11B</th>\n",
       "      <th>GABRD</th>\n",
       "      <th>HES5</th>\n",
       "      <th>...</th>\n",
       "      <th>ITGB2-AS1</th>\n",
       "      <th>COL18A1</th>\n",
       "      <th>COL6A1</th>\n",
       "      <th>COL6A2</th>\n",
       "      <th>S100B</th>\n",
       "      <th>MT-ND2</th>\n",
       "      <th>MT-CO1</th>\n",
       "      <th>MT-ND5</th>\n",
       "      <th>MT-ND6</th>\n",
       "      <th>AC233755.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACCTGTCATCGCTC</th>\n",
       "      <td>-0.311436</td>\n",
       "      <td>-0.201189</td>\n",
       "      <td>-0.232595</td>\n",
       "      <td>-0.051972</td>\n",
       "      <td>-0.194643</td>\n",
       "      <td>-0.450108</td>\n",
       "      <td>-0.328487</td>\n",
       "      <td>2.059860</td>\n",
       "      <td>-0.049568</td>\n",
       "      <td>-0.034263</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.177449</td>\n",
       "      <td>-0.666466</td>\n",
       "      <td>-0.754597</td>\n",
       "      <td>-0.828566</td>\n",
       "      <td>-0.113428</td>\n",
       "      <td>0.553124</td>\n",
       "      <td>-0.075820</td>\n",
       "      <td>-0.774423</td>\n",
       "      <td>-0.416498</td>\n",
       "      <td>-0.005798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCAAATCA</th>\n",
       "      <td>-0.255890</td>\n",
       "      <td>-0.144352</td>\n",
       "      <td>-0.189365</td>\n",
       "      <td>-0.035611</td>\n",
       "      <td>-0.143560</td>\n",
       "      <td>-0.371510</td>\n",
       "      <td>-0.261193</td>\n",
       "      <td>-0.281206</td>\n",
       "      <td>-0.041230</td>\n",
       "      <td>-0.028504</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.122380</td>\n",
       "      <td>-0.550524</td>\n",
       "      <td>-0.610986</td>\n",
       "      <td>-0.655446</td>\n",
       "      <td>-0.088170</td>\n",
       "      <td>-0.529112</td>\n",
       "      <td>-0.803119</td>\n",
       "      <td>0.921478</td>\n",
       "      <td>-0.339208</td>\n",
       "      <td>-0.005312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCAACGGT</th>\n",
       "      <td>-0.227129</td>\n",
       "      <td>-0.110723</td>\n",
       "      <td>-0.167058</td>\n",
       "      <td>-0.025548</td>\n",
       "      <td>-0.112809</td>\n",
       "      <td>-0.330227</td>\n",
       "      <td>-0.226142</td>\n",
       "      <td>-0.246232</td>\n",
       "      <td>-0.037028</td>\n",
       "      <td>-0.025578</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.088409</td>\n",
       "      <td>-0.483794</td>\n",
       "      <td>-0.522890</td>\n",
       "      <td>-0.544920</td>\n",
       "      <td>-0.074377</td>\n",
       "      <td>1.581135</td>\n",
       "      <td>-0.411826</td>\n",
       "      <td>-0.080644</td>\n",
       "      <td>-0.298660</td>\n",
       "      <td>-0.005015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCCCAATT</th>\n",
       "      <td>4.397746</td>\n",
       "      <td>-0.188441</td>\n",
       "      <td>-0.270080</td>\n",
       "      <td>-0.041637</td>\n",
       "      <td>-0.175621</td>\n",
       "      <td>-0.516146</td>\n",
       "      <td>-0.385914</td>\n",
       "      <td>-0.405673</td>\n",
       "      <td>-0.057058</td>\n",
       "      <td>-0.039409</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.145048</td>\n",
       "      <td>-0.718326</td>\n",
       "      <td>-0.042139</td>\n",
       "      <td>-0.765045</td>\n",
       "      <td>-0.121027</td>\n",
       "      <td>-0.711507</td>\n",
       "      <td>-0.152514</td>\n",
       "      <td>-0.190425</td>\n",
       "      <td>-0.481579</td>\n",
       "      <td>-0.006150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGCAGGTCCAC</th>\n",
       "      <td>-0.248461</td>\n",
       "      <td>-0.119984</td>\n",
       "      <td>-0.183599</td>\n",
       "      <td>-0.026746</td>\n",
       "      <td>-0.119609</td>\n",
       "      <td>-0.360877</td>\n",
       "      <td>-0.252150</td>\n",
       "      <td>-0.272183</td>\n",
       "      <td>-0.040138</td>\n",
       "      <td>-0.027745</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.093342</td>\n",
       "      <td>-0.523794</td>\n",
       "      <td>-0.560482</td>\n",
       "      <td>-0.577435</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>-0.514556</td>\n",
       "      <td>0.029385</td>\n",
       "      <td>1.298649</td>\n",
       "      <td>-0.328761</td>\n",
       "      <td>-0.005239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGGTTAGTGTGGCA</th>\n",
       "      <td>-0.368990</td>\n",
       "      <td>-0.196373</td>\n",
       "      <td>-0.277669</td>\n",
       "      <td>-0.043774</td>\n",
       "      <td>-0.182503</td>\n",
       "      <td>-0.529225</td>\n",
       "      <td>3.232180</td>\n",
       "      <td>-0.417152</td>\n",
       "      <td>-0.058610</td>\n",
       "      <td>-0.040474</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.152073</td>\n",
       "      <td>-0.734129</td>\n",
       "      <td>0.566377</td>\n",
       "      <td>-0.186295</td>\n",
       "      <td>-0.125127</td>\n",
       "      <td>-0.726397</td>\n",
       "      <td>-0.211839</td>\n",
       "      <td>-0.226750</td>\n",
       "      <td>-0.494488</td>\n",
       "      <td>-0.006215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGGTTGTACACCGC</th>\n",
       "      <td>-0.243327</td>\n",
       "      <td>-0.112690</td>\n",
       "      <td>-0.179616</td>\n",
       "      <td>-0.024433</td>\n",
       "      <td>-0.112803</td>\n",
       "      <td>-0.353515</td>\n",
       "      <td>-0.245896</td>\n",
       "      <td>-0.265943</td>\n",
       "      <td>-0.039387</td>\n",
       "      <td>-0.027222</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.085611</td>\n",
       "      <td>-0.511145</td>\n",
       "      <td>-0.542544</td>\n",
       "      <td>-0.553725</td>\n",
       "      <td>-0.078069</td>\n",
       "      <td>-0.504400</td>\n",
       "      <td>0.559458</td>\n",
       "      <td>0.011658</td>\n",
       "      <td>-0.321530</td>\n",
       "      <td>-0.005187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGGTTTCTGATACG</th>\n",
       "      <td>-0.347610</td>\n",
       "      <td>-0.196337</td>\n",
       "      <td>-0.260886</td>\n",
       "      <td>-0.046053</td>\n",
       "      <td>-0.185155</td>\n",
       "      <td>-0.500164</td>\n",
       "      <td>-0.371923</td>\n",
       "      <td>1.625337</td>\n",
       "      <td>-0.055195</td>\n",
       "      <td>-0.038130</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.159158</td>\n",
       "      <td>-0.709489</td>\n",
       "      <td>-0.763816</td>\n",
       "      <td>-0.797173</td>\n",
       "      <td>-0.120261</td>\n",
       "      <td>-0.692930</td>\n",
       "      <td>-0.350401</td>\n",
       "      <td>0.278521</td>\n",
       "      <td>-0.465814</td>\n",
       "      <td>-0.006068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGTCAAGTGAATTG</th>\n",
       "      <td>-0.364469</td>\n",
       "      <td>-0.215867</td>\n",
       "      <td>-0.274116</td>\n",
       "      <td>-0.052037</td>\n",
       "      <td>-0.202972</td>\n",
       "      <td>-0.523115</td>\n",
       "      <td>-0.392036</td>\n",
       "      <td>-0.411783</td>\n",
       "      <td>-0.057882</td>\n",
       "      <td>-0.039974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.178774</td>\n",
       "      <td>-0.741183</td>\n",
       "      <td>-0.804671</td>\n",
       "      <td>-0.849013</td>\n",
       "      <td>-0.128745</td>\n",
       "      <td>-0.719476</td>\n",
       "      <td>-0.009504</td>\n",
       "      <td>-0.555168</td>\n",
       "      <td>-0.488457</td>\n",
       "      <td>-0.006185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGTCAGTAAGAGAG</th>\n",
       "      <td>-0.217620</td>\n",
       "      <td>-0.156840</td>\n",
       "      <td>-0.159691</td>\n",
       "      <td>-0.045082</td>\n",
       "      <td>-0.161068</td>\n",
       "      <td>-0.316521</td>\n",
       "      <td>-0.214532</td>\n",
       "      <td>-0.234647</td>\n",
       "      <td>-0.035653</td>\n",
       "      <td>-0.024616</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.151136</td>\n",
       "      <td>-0.496746</td>\n",
       "      <td>-0.595599</td>\n",
       "      <td>-0.689577</td>\n",
       "      <td>-0.083524</td>\n",
       "      <td>-0.452480</td>\n",
       "      <td>0.137370</td>\n",
       "      <td>-0.837553</td>\n",
       "      <td>-0.285202</td>\n",
       "      <td>-0.004909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47060 rows × 3765 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  HES4     ISG15      AGRN  TNFRSF18  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC -0.311436 -0.201189 -0.232595 -0.051972   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA -0.255890 -0.144352 -0.189365 -0.035611   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT -0.227129 -0.110723 -0.167058 -0.025548   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT  4.397746 -0.188441 -0.270080 -0.041637   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC -0.248461 -0.119984 -0.183599 -0.026746   \n",
       "...                                ...       ...       ...       ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA  -0.368990 -0.196373 -0.277669 -0.043774   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC  -0.243327 -0.112690 -0.179616 -0.024433   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG  -0.347610 -0.196337 -0.260886 -0.046053   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG  -0.364469 -0.215867 -0.274116 -0.052037   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG  -0.217620 -0.156840 -0.159691 -0.045082   \n",
       "\n",
       "                               TNFRSF4     MXRA8    MMP23B    CDK11B  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC -0.194643 -0.450108 -0.328487  2.059860   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA -0.143560 -0.371510 -0.261193 -0.281206   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT -0.112809 -0.330227 -0.226142 -0.246232   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT -0.175621 -0.516146 -0.385914 -0.405673   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC -0.119609 -0.360877 -0.252150 -0.272183   \n",
       "...                                ...       ...       ...       ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA  -0.182503 -0.529225  3.232180 -0.417152   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC  -0.112803 -0.353515 -0.245896 -0.265943   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG  -0.185155 -0.500164 -0.371923  1.625337   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG  -0.202972 -0.523115 -0.392036 -0.411783   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG  -0.161068 -0.316521 -0.214532 -0.234647   \n",
       "\n",
       "                                 GABRD      HES5  ...  ITGB2-AS1   COL18A1  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC -0.049568 -0.034263  ...  -0.177449 -0.666466   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA -0.041230 -0.028504  ...  -0.122380 -0.550524   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT -0.037028 -0.025578  ...  -0.088409 -0.483794   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT -0.057058 -0.039409  ...  -0.145048 -0.718326   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC -0.040138 -0.027745  ...  -0.093342 -0.523794   \n",
       "...                                ...       ...  ...        ...       ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA  -0.058610 -0.040474  ...  -0.152073 -0.734129   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC  -0.039387 -0.027222  ...  -0.085611 -0.511145   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG  -0.055195 -0.038130  ...  -0.159158 -0.709489   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG  -0.057882 -0.039974  ...  -0.178774 -0.741183   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG  -0.035653 -0.024616  ...  -0.151136 -0.496746   \n",
       "\n",
       "                                COL6A1    COL6A2     S100B    MT-ND2  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC -0.754597 -0.828566 -0.113428  0.553124   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA -0.610986 -0.655446 -0.088170 -0.529112   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT -0.522890 -0.544920 -0.074377  1.581135   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT -0.042139 -0.765045 -0.121027 -0.711507   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC -0.560482 -0.577435 -0.080845 -0.514556   \n",
       "...                                ...       ...       ...       ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA   0.566377 -0.186295 -0.125127 -0.726397   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC  -0.542544 -0.553725 -0.078069 -0.504400   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG  -0.763816 -0.797173 -0.120261 -0.692930   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG  -0.804671 -0.849013 -0.128745 -0.719476   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG  -0.595599 -0.689577 -0.083524 -0.452480   \n",
       "\n",
       "                                MT-CO1    MT-ND5    MT-ND6  AC233755.1  \n",
       "H_ZC-11-292_AAACCTGTCATCGCTC -0.075820 -0.774423 -0.416498   -0.005798  \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA -0.803119  0.921478 -0.339208   -0.005312  \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT -0.411826 -0.080644 -0.298660   -0.005015  \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT -0.152514 -0.190425 -0.481579   -0.006150  \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC  0.029385  1.298649 -0.328761   -0.005239  \n",
       "...                                ...       ...       ...         ...  \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA  -0.211839 -0.226750 -0.494488   -0.006215  \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC   0.559458  0.011658 -0.321530   -0.005187  \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG  -0.350401  0.278521 -0.465814   -0.006068  \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG  -0.009504 -0.555168 -0.488457   -0.006185  \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG   0.137370 -0.837553 -0.285202   -0.004909  \n",
       "\n",
       "[47060 rows x 3765 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sct_normalized_matrix = './cardio_SCT_normalized.txt'\n",
    "counts = pd.read_csv(sct_normalized_matrix, sep=',', index_col=0).transpose()\n",
    "counts = counts.sort_index()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orig_ident</th>\n",
       "      <th>nCount_RNA</th>\n",
       "      <th>nFeature_RNA</th>\n",
       "      <th>percent.mito</th>\n",
       "      <th>nCount_SCT</th>\n",
       "      <th>nFeature_SCT</th>\n",
       "      <th>SCT_snn_res.0.03</th>\n",
       "      <th>SCT_snn_res.0.04</th>\n",
       "      <th>SCT_snn_res.0.05</th>\n",
       "      <th>SCT_snn_res.0.06</th>\n",
       "      <th>...</th>\n",
       "      <th>Age_Group_Tertile</th>\n",
       "      <th>Names</th>\n",
       "      <th>SCT_snn_res.0.01</th>\n",
       "      <th>SCT_snn_res.0.02</th>\n",
       "      <th>nCount_prediction.score.celltype.l1</th>\n",
       "      <th>nFeature_prediction.score.celltype.l1</th>\n",
       "      <th>nCount_prediction.score.celltype.l2</th>\n",
       "      <th>nFeature_prediction.score.celltype.l2</th>\n",
       "      <th>Cm_Names</th>\n",
       "      <th>CondIdent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACCTGTCATCGCTC</th>\n",
       "      <td>H_ZC-11-292</td>\n",
       "      <td>4658</td>\n",
       "      <td>1862</td>\n",
       "      <td>0.536711</td>\n",
       "      <td>2794</td>\n",
       "      <td>1772</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Old</td>\n",
       "      <td>Cardiomyocytes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cm2 - ACTA1</td>\n",
       "      <td>Donor_H_ZC-11-292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCAAATCA</th>\n",
       "      <td>H_ZC-11-292</td>\n",
       "      <td>2400</td>\n",
       "      <td>944</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2283</td>\n",
       "      <td>944</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Old</td>\n",
       "      <td>Cardiomyocytes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cm2 - ACTA1</td>\n",
       "      <td>Donor_H_ZC-11-292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCAACGGT</th>\n",
       "      <td>H_ZC-11-292</td>\n",
       "      <td>1620</td>\n",
       "      <td>692</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>1980</td>\n",
       "      <td>692</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Old</td>\n",
       "      <td>Cardiomyocytes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cm2 - ACTA1</td>\n",
       "      <td>Donor_H_ZC-11-292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCCCAATT</th>\n",
       "      <td>H_ZC-11-292</td>\n",
       "      <td>7737</td>\n",
       "      <td>2338</td>\n",
       "      <td>0.090474</td>\n",
       "      <td>2516</td>\n",
       "      <td>1417</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Old</td>\n",
       "      <td>Cardiomyocytes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cm6 - NPPA/NPPB</td>\n",
       "      <td>Donor_H_ZC-11-292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGCAGGTCCAC</th>\n",
       "      <td>H_ZC-11-292</td>\n",
       "      <td>2177</td>\n",
       "      <td>878</td>\n",
       "      <td>0.137804</td>\n",
       "      <td>2182</td>\n",
       "      <td>878</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Old</td>\n",
       "      <td>Cardiomyocytes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cm2 - ACTA1</td>\n",
       "      <td>Donor_H_ZC-11-292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGGTTAGTGTGGCA</th>\n",
       "      <td>TWCM-LVAD3</td>\n",
       "      <td>8531</td>\n",
       "      <td>2995</td>\n",
       "      <td>0.105498</td>\n",
       "      <td>2700</td>\n",
       "      <td>1712</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Middle</td>\n",
       "      <td>Cardiomyocytes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cm2 - ACTA1</td>\n",
       "      <td>DCM_TWCM-LVAD3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGGTTGTACACCGC</th>\n",
       "      <td>TWCM-LVAD3</td>\n",
       "      <td>2032</td>\n",
       "      <td>842</td>\n",
       "      <td>0.098425</td>\n",
       "      <td>2095</td>\n",
       "      <td>842</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Middle</td>\n",
       "      <td>Cardiomyocytes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cm4 - ADGRL3</td>\n",
       "      <td>DCM_TWCM-LVAD3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGGTTTCTGATACG</th>\n",
       "      <td>TWCM-LVAD3</td>\n",
       "      <td>6860</td>\n",
       "      <td>1939</td>\n",
       "      <td>0.247813</td>\n",
       "      <td>2783</td>\n",
       "      <td>1350</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Middle</td>\n",
       "      <td>Cardiomyocytes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cm2 - ACTA1</td>\n",
       "      <td>DCM_TWCM-LVAD3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGTCAAGTGAATTG</th>\n",
       "      <td>TWCM-LVAD3</td>\n",
       "      <td>8151</td>\n",
       "      <td>2677</td>\n",
       "      <td>0.331248</td>\n",
       "      <td>2754</td>\n",
       "      <td>1610</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Middle</td>\n",
       "      <td>Cardiomyocytes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cm2 - ACTA1</td>\n",
       "      <td>DCM_TWCM-LVAD3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGTCAGTAAGAGAG</th>\n",
       "      <td>TWCM-LVAD3</td>\n",
       "      <td>1408</td>\n",
       "      <td>830</td>\n",
       "      <td>0.710227</td>\n",
       "      <td>1791</td>\n",
       "      <td>830</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Middle</td>\n",
       "      <td>Cardiomyocytes</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cm4 - ADGRL3</td>\n",
       "      <td>DCM_TWCM-LVAD3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47060 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               orig_ident  nCount_RNA  nFeature_RNA  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC  H_ZC-11-292        4658          1862   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA  H_ZC-11-292        2400           944   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT  H_ZC-11-292        1620           692   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT  H_ZC-11-292        7737          2338   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC  H_ZC-11-292        2177           878   \n",
       "...                                   ...         ...           ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA    TWCM-LVAD3        8531          2995   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC    TWCM-LVAD3        2032           842   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG    TWCM-LVAD3        6860          1939   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG    TWCM-LVAD3        8151          2677   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG    TWCM-LVAD3        1408           830   \n",
       "\n",
       "                              percent.mito  nCount_SCT  nFeature_SCT  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC      0.536711        2794          1772   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA      0.333333        2283           944   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT      0.185185        1980           692   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT      0.090474        2516          1417   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC      0.137804        2182           878   \n",
       "...                                    ...         ...           ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA       0.105498        2700          1712   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC       0.098425        2095           842   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG       0.247813        2783          1350   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG       0.331248        2754          1610   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG       0.710227        1791           830   \n",
       "\n",
       "                              SCT_snn_res.0.03  SCT_snn_res.0.04  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC                 2                 2   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA                 2                 2   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT                 2                 2   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT                 2                 2   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC                 2                 2   \n",
       "...                                        ...               ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA                  2                 2   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC                  2                 2   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG                  2                 2   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG                  2                 2   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG                  2                 2   \n",
       "\n",
       "                              SCT_snn_res.0.05  SCT_snn_res.0.06  ...  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC                 2                 2  ...   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA                 2                 2  ...   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT                 2                 2  ...   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT                 2                 2  ...   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC                 2                 2  ...   \n",
       "...                                        ...               ...  ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA                  2                 2  ...   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC                  2                 2  ...   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG                  2                 2  ...   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG                  2                 2  ...   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG                  2                 2  ...   \n",
       "\n",
       "                              Age_Group_Tertile           Names  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC                Old  Cardiomyocytes   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA                Old  Cardiomyocytes   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT                Old  Cardiomyocytes   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT                Old  Cardiomyocytes   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC                Old  Cardiomyocytes   \n",
       "...                                         ...             ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA              Middle  Cardiomyocytes   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC              Middle  Cardiomyocytes   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG              Middle  Cardiomyocytes   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG              Middle  Cardiomyocytes   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG              Middle  Cardiomyocytes   \n",
       "\n",
       "                              SCT_snn_res.0.01  SCT_snn_res.0.02  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC                 2                 2   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA                 2                 2   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT                 2                 2   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT                 2                 2   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC                 2                 2   \n",
       "...                                        ...               ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA                  2                 2   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC                  2                 2   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG                  2                 2   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG                  2                 2   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG                  2                 2   \n",
       "\n",
       "                              nCount_prediction.score.celltype.l1  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC                                    0   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA                                    0   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT                                    0   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT                                    0   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC                                    0   \n",
       "...                                                           ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA                                     0   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC                                     0   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG                                     0   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG                                     0   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG                                     0   \n",
       "\n",
       "                              nFeature_prediction.score.celltype.l1  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC                                      0   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA                                      0   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT                                      0   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT                                      0   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC                                      0   \n",
       "...                                                             ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA                                       0   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC                                       0   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG                                       0   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG                                       0   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG                                       0   \n",
       "\n",
       "                              nCount_prediction.score.celltype.l2  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC                                    0   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA                                    0   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT                                    0   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT                                    0   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC                                    0   \n",
       "...                                                           ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA                                     0   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC                                     0   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG                                     0   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG                                     0   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG                                     0   \n",
       "\n",
       "                              nFeature_prediction.score.celltype.l2  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC                                      0   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA                                      0   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT                                      0   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT                                      0   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC                                      0   \n",
       "...                                                             ...   \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA                                       0   \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC                                       0   \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG                                       0   \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG                                       0   \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG                                       0   \n",
       "\n",
       "                                     Cm_Names          CondIdent  \n",
       "H_ZC-11-292_AAACCTGTCATCGCTC      Cm2 - ACTA1  Donor_H_ZC-11-292  \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA      Cm2 - ACTA1  Donor_H_ZC-11-292  \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT      Cm2 - ACTA1  Donor_H_ZC-11-292  \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT  Cm6 - NPPA/NPPB  Donor_H_ZC-11-292  \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC      Cm2 - ACTA1  Donor_H_ZC-11-292  \n",
       "...                                       ...                ...  \n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA       Cm2 - ACTA1     DCM_TWCM-LVAD3  \n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC      Cm4 - ADGRL3     DCM_TWCM-LVAD3  \n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG       Cm2 - ACTA1     DCM_TWCM-LVAD3  \n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG       Cm2 - ACTA1     DCM_TWCM-LVAD3  \n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG      Cm4 - ADGRL3     DCM_TWCM-LVAD3  \n",
       "\n",
       "[47060 rows x 59 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_data = pd.read_csv('./cardio_meta.csv', index_col=0)\n",
    "meta_data = meta_data.sort_index()\n",
    "meta_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fig, ax = palantir.plot.plot_molecules_per_cell_and_gene(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Determing nearest neighbor graph...\n"
     ]
    }
   ],
   "source": [
    "pca_projections, _ = palantir.utils.run_pca(counts, use_hvg=False)\n",
    "dm_res = palantir.utils.run_diffusion_maps(pca_projections)\n",
    "ms_data = palantir.utils.determine_multiscale_space(dm_res) #n_eigs=5\n",
    "tsne = palantir.utils.run_tsne(ms_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 500/500 [15:59<00:00,  1.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BarnesHut Approximation  took  211.27  seconds\n",
      "Repulsion forces  took  715.31  seconds\n",
      "Gravitational forces  took  2.95  seconds\n",
      "Attraction forces  took  15.36  seconds\n",
      "AdjustSpeedAndApplyForces step  took  7.73  seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "fdl = harmony.plot.force_directed_layout(dm_res['kernel'], counts.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = palantir.plot.plot_tsne(tsne)\n",
    "plt.savefig('tsne.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = palantir.plot.plot_tsne_by_cell_sizes(counts, tsne)\n",
    "plt.savefig('tsne_counts.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_df = palantir.utils.run_magic_imputation(counts, dm_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Bitstream Vera Sans'] not found. Falling back to DejaVu Sans.\n"
     ]
    }
   ],
   "source": [
    "palantir.plot.plot_diffusion_components(tsne, dm_res)\n",
    "plt.savefig('tsne_diff_components.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "palantir.plot.plot_cell_clusters(fdl, meta_data[\"Cm_Names\"])\n",
    "plt.savefig('fdl_annotations.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "palantir.plot.plot_cell_clusters(fdl, meta_data[\"condition\"])\n",
    "plt.savefig('fdl_condition.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',\\n       ...\\n       '47050', '47051', '47052', '47053', '47054', '47055', '47056', '47057',\\n       '47058', '47059'],\\n      dtype='object', length=47060)] are in the [index]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-0e2bb82beeef>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpalantir\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_cell_clusters\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtsne\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeta_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"condition\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tsne_condition.png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\palantir\\plot.py\u001b[0m in \u001b[0;36mplot_cell_clusters\u001b[1;34m(tsne, clusters)\u001b[0m\n\u001b[0;32m    185\u001b[0m     \u001b[1;31m# Clusters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    186\u001b[0m     \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m     \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtsne\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"x\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtsne\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcluster_colors\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mclusters\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtsne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    188\u001b[0m     \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis_off\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    904\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    905\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 906\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    908\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_with\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    944\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    945\u001b[0m         \u001b[1;31m# handle the dup indexing case GH#4246\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 946\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    947\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    948\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_values_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    877\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 879\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    880\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    881\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1097\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot index with multidimensional key\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1098\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1099\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1101\u001b[0m             \u001b[1;31m# nested tuple slicing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1035\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1036\u001b[0m         \u001b[1;31m# A collection of keys\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1037\u001b[1;33m         \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1038\u001b[0m         return self.obj._reindex_with_indexers(\n\u001b[0;32m   1039\u001b[0m             \u001b[1;33m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1252\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1253\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1254\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1255\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1296\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmissing\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1297\u001b[0m                 \u001b[0maxis_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1298\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1299\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1300\u001b[0m             \u001b[1;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',\\n       ...\\n       '47050', '47051', '47052', '47053', '47054', '47055', '47056', '47057',\\n       '47058', '47059'],\\n      dtype='object', length=47060)] are in the [index]\""
     ]
    }
   ],
   "source": [
    "palantir.plot.plot_cell_clusters(tsne, meta_data[\"condition\"])\n",
    "plt.savefig('tsne_condition.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_cell = \"TWCM-13-96_TCTTCGGAGCCACTAT\"\n",
    "palantir.plot.highlight_cells_on_tsne(fdl, [start_cell])\n",
    "plt.savefig('fdl_start_cell.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACCTGTCATCGCTC</th>\n",
       "      <td>-0.071976</td>\n",
       "      <td>0.459360</td>\n",
       "      <td>-0.152989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCAAATCA</th>\n",
       "      <td>-0.064433</td>\n",
       "      <td>0.350535</td>\n",
       "      <td>-0.117321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCAACGGT</th>\n",
       "      <td>-0.054814</td>\n",
       "      <td>0.190717</td>\n",
       "      <td>-0.076491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCCCAATT</th>\n",
       "      <td>-0.087890</td>\n",
       "      <td>0.675182</td>\n",
       "      <td>-0.119854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGCAGGTCCAC</th>\n",
       "      <td>-0.081474</td>\n",
       "      <td>0.585001</td>\n",
       "      <td>-0.200357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGGTTAGTGTGGCA</th>\n",
       "      <td>-0.033985</td>\n",
       "      <td>0.010602</td>\n",
       "      <td>0.004376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGGTTGTACACCGC</th>\n",
       "      <td>-0.036795</td>\n",
       "      <td>0.011449</td>\n",
       "      <td>0.000476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGGTTTCTGATACG</th>\n",
       "      <td>-0.052687</td>\n",
       "      <td>0.167579</td>\n",
       "      <td>-0.056249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGTCAAGTGAATTG</th>\n",
       "      <td>-0.036957</td>\n",
       "      <td>0.006308</td>\n",
       "      <td>-0.003522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-LVAD3_TTTGTCAGTAAGAGAG</th>\n",
       "      <td>-0.039691</td>\n",
       "      <td>0.010728</td>\n",
       "      <td>-0.005179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47060 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0         1         2\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC -0.071976  0.459360 -0.152989\n",
       "H_ZC-11-292_AAACGGGAGCAAATCA -0.064433  0.350535 -0.117321\n",
       "H_ZC-11-292_AAACGGGAGCAACGGT -0.054814  0.190717 -0.076491\n",
       "H_ZC-11-292_AAACGGGAGCCCAATT -0.087890  0.675182 -0.119854\n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC -0.081474  0.585001 -0.200357\n",
       "...                                ...       ...       ...\n",
       "TWCM-LVAD3_TTTGGTTAGTGTGGCA  -0.033985  0.010602  0.004376\n",
       "TWCM-LVAD3_TTTGGTTGTACACCGC  -0.036795  0.011449  0.000476\n",
       "TWCM-LVAD3_TTTGGTTTCTGATACG  -0.052687  0.167579 -0.056249\n",
       "TWCM-LVAD3_TTTGTCAAGTGAATTG  -0.036957  0.006308 -0.003522\n",
       "TWCM-LVAD3_TTTGTCAGTAAGAGAG  -0.039691  0.010728 -0.005179\n",
       "\n",
       "[47060 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1=counts.index.values.tolist()\n",
    "ms_data.index=list1\n",
    "ms_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampling and flocking waypoints...\n",
      "Time for determining waypoints: 0.2627596855163574 minutes\n",
      "Determining pseudotime...\n",
      "Shortest path distances using 30-nearest neighbor graph...\n",
      "Time for shortest paths: 1.572943623860677 minutes\n",
      "Iteratively refining the pseudotime...\n",
      "Correlation at iteration 1: 1.0000\n",
      "Entropy and branch probabilities...\n",
      "Markov chain construction...\n",
      "Identification of terminal states...\n",
      "Computing fundamental matrix and absorption probabilities...\n",
      "Project results to all cells...\n"
     ]
    }
   ],
   "source": [
    "pr_res = palantir.core.run_palantir(ms_data, start_cell, num_waypoints=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cm2 - ACTA1\n",
      "Cm6 - NPPA/NPPB\n",
      "Cm3 - MYL7\n"
     ]
    }
   ],
   "source": [
    "lst = []\n",
    "for i in pr_res.branch_probs.columns:\n",
    "    print(meta_data.Cm_Names[i])\n",
    "    lst.append(meta_data.Cm_Names[i])\n",
    "\n",
    "pr_res.branch_probs.columns = lst\n",
    "pr_res.branch_probs = pr_res.branch_probs.loc[:, lst]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "palantir.plot.plot_palantir_results(pr_res, fdl)\n",
    "plt.savefig('palantir_summary.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MonoD28_TGCGCAGTCTTCGGTC-1</th>\n",
       "      <th>MonoD3_ACGGCCAGTAGAAGGA-1</th>\n",
       "      <th>MonoD3_CAAGAAAGTGCGATAG-1</th>\n",
       "      <th>MonoD7_GTCACAACATGTCCTC-1</th>\n",
       "      <th>pseudotime</th>\n",
       "      <th>entropy</th>\n",
       "      <th>ClusterName</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MonoD13_AAACCTGAGCGATCCC-1</th>\n",
       "      <td>0.571238</td>\n",
       "      <td>0.029186</td>\n",
       "      <td>0.166785</td>\n",
       "      <td>0.232791</td>\n",
       "      <td>0.509674</td>\n",
       "      <td>1.061049</td>\n",
       "      <td>MHC_High</td>\n",
       "      <td>MonoD13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD13_AAACCTGAGGCAGTCA-1</th>\n",
       "      <td>0.440454</td>\n",
       "      <td>0.038028</td>\n",
       "      <td>0.217303</td>\n",
       "      <td>0.304215</td>\n",
       "      <td>0.519599</td>\n",
       "      <td>1.179206</td>\n",
       "      <td>Lyve1</td>\n",
       "      <td>MonoD13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD13_AAACCTGCATCCCATC-1</th>\n",
       "      <td>0.299583</td>\n",
       "      <td>0.046460</td>\n",
       "      <td>0.265483</td>\n",
       "      <td>0.388474</td>\n",
       "      <td>0.481260</td>\n",
       "      <td>1.223097</td>\n",
       "      <td>Trem2</td>\n",
       "      <td>MonoD13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD13_AAACCTGCATTCCTCG-1</th>\n",
       "      <td>0.371367</td>\n",
       "      <td>0.042899</td>\n",
       "      <td>0.245132</td>\n",
       "      <td>0.340603</td>\n",
       "      <td>0.489879</td>\n",
       "      <td>1.214435</td>\n",
       "      <td>Cxcl1</td>\n",
       "      <td>MonoD13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD13_AAACCTGGTAAATACG-1</th>\n",
       "      <td>0.476905</td>\n",
       "      <td>0.035609</td>\n",
       "      <td>0.203486</td>\n",
       "      <td>0.284000</td>\n",
       "      <td>0.483772</td>\n",
       "      <td>1.153357</td>\n",
       "      <td>DCs</td>\n",
       "      <td>MonoD13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD7_TTTGTCAGTTCGCGAC-1</th>\n",
       "      <td>0.304282</td>\n",
       "      <td>0.045997</td>\n",
       "      <td>0.262841</td>\n",
       "      <td>0.386880</td>\n",
       "      <td>0.537449</td>\n",
       "      <td>1.222275</td>\n",
       "      <td>Trem2</td>\n",
       "      <td>MonoD7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD7_TTTGTCATCATCACCC-1</th>\n",
       "      <td>0.402353</td>\n",
       "      <td>0.040759</td>\n",
       "      <td>0.232907</td>\n",
       "      <td>0.323981</td>\n",
       "      <td>0.499346</td>\n",
       "      <td>1.201266</td>\n",
       "      <td>MHC_High</td>\n",
       "      <td>MonoD7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD7_TTTGTCATCGATAGAA-1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.999945</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.964117</td>\n",
       "      <td>0.000633</td>\n",
       "      <td>Ccrl2_Neutrophils</td>\n",
       "      <td>MonoD7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD7_TTTGTCATCTGGAGCC-1</th>\n",
       "      <td>0.310825</td>\n",
       "      <td>0.046066</td>\n",
       "      <td>0.263233</td>\n",
       "      <td>0.379875</td>\n",
       "      <td>0.494633</td>\n",
       "      <td>1.224011</td>\n",
       "      <td>Cxcl1</td>\n",
       "      <td>MonoD7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD7_TTTGTCATCTTGAGGT-1</th>\n",
       "      <td>0.303476</td>\n",
       "      <td>0.047537</td>\n",
       "      <td>0.271637</td>\n",
       "      <td>0.377349</td>\n",
       "      <td>0.500357</td>\n",
       "      <td>1.228471</td>\n",
       "      <td>Cxcl1</td>\n",
       "      <td>MonoD7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16217 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            MonoD28_TGCGCAGTCTTCGGTC-1  \\\n",
       "MonoD13_AAACCTGAGCGATCCC-1                    0.571238   \n",
       "MonoD13_AAACCTGAGGCAGTCA-1                    0.440454   \n",
       "MonoD13_AAACCTGCATCCCATC-1                    0.299583   \n",
       "MonoD13_AAACCTGCATTCCTCG-1                    0.371367   \n",
       "MonoD13_AAACCTGGTAAATACG-1                    0.476905   \n",
       "...                                                ...   \n",
       "MonoD7_TTTGTCAGTTCGCGAC-1                     0.304282   \n",
       "MonoD7_TTTGTCATCATCACCC-1                     0.402353   \n",
       "MonoD7_TTTGTCATCGATAGAA-1                     0.000000   \n",
       "MonoD7_TTTGTCATCTGGAGCC-1                     0.310825   \n",
       "MonoD7_TTTGTCATCTTGAGGT-1                     0.303476   \n",
       "\n",
       "                            MonoD3_ACGGCCAGTAGAAGGA-1  \\\n",
       "MonoD13_AAACCTGAGCGATCCC-1                   0.029186   \n",
       "MonoD13_AAACCTGAGGCAGTCA-1                   0.038028   \n",
       "MonoD13_AAACCTGCATCCCATC-1                   0.046460   \n",
       "MonoD13_AAACCTGCATTCCTCG-1                   0.042899   \n",
       "MonoD13_AAACCTGGTAAATACG-1                   0.035609   \n",
       "...                                               ...   \n",
       "MonoD7_TTTGTCAGTTCGCGAC-1                    0.045997   \n",
       "MonoD7_TTTGTCATCATCACCC-1                    0.040759   \n",
       "MonoD7_TTTGTCATCGATAGAA-1                    0.999945   \n",
       "MonoD7_TTTGTCATCTGGAGCC-1                    0.046066   \n",
       "MonoD7_TTTGTCATCTTGAGGT-1                    0.047537   \n",
       "\n",
       "                            MonoD3_CAAGAAAGTGCGATAG-1  \\\n",
       "MonoD13_AAACCTGAGCGATCCC-1                   0.166785   \n",
       "MonoD13_AAACCTGAGGCAGTCA-1                   0.217303   \n",
       "MonoD13_AAACCTGCATCCCATC-1                   0.265483   \n",
       "MonoD13_AAACCTGCATTCCTCG-1                   0.245132   \n",
       "MonoD13_AAACCTGGTAAATACG-1                   0.203486   \n",
       "...                                               ...   \n",
       "MonoD7_TTTGTCAGTTCGCGAC-1                    0.262841   \n",
       "MonoD7_TTTGTCATCATCACCC-1                    0.232907   \n",
       "MonoD7_TTTGTCATCGATAGAA-1                    0.000000   \n",
       "MonoD7_TTTGTCATCTGGAGCC-1                    0.263233   \n",
       "MonoD7_TTTGTCATCTTGAGGT-1                    0.271637   \n",
       "\n",
       "                            MonoD7_GTCACAACATGTCCTC-1  pseudotime   entropy  \\\n",
       "MonoD13_AAACCTGAGCGATCCC-1                   0.232791    0.509674  1.061049   \n",
       "MonoD13_AAACCTGAGGCAGTCA-1                   0.304215    0.519599  1.179206   \n",
       "MonoD13_AAACCTGCATCCCATC-1                   0.388474    0.481260  1.223097   \n",
       "MonoD13_AAACCTGCATTCCTCG-1                   0.340603    0.489879  1.214435   \n",
       "MonoD13_AAACCTGGTAAATACG-1                   0.284000    0.483772  1.153357   \n",
       "...                                               ...         ...       ...   \n",
       "MonoD7_TTTGTCAGTTCGCGAC-1                    0.386880    0.537449  1.222275   \n",
       "MonoD7_TTTGTCATCATCACCC-1                    0.323981    0.499346  1.201266   \n",
       "MonoD7_TTTGTCATCGATAGAA-1                    0.000000    0.964117  0.000633   \n",
       "MonoD7_TTTGTCATCTGGAGCC-1                    0.379875    0.494633  1.224011   \n",
       "MonoD7_TTTGTCATCTTGAGGT-1                    0.377349    0.500357  1.228471   \n",
       "\n",
       "                                  ClusterName Condition  \n",
       "MonoD13_AAACCTGAGCGATCCC-1           MHC_High   MonoD13  \n",
       "MonoD13_AAACCTGAGGCAGTCA-1              Lyve1   MonoD13  \n",
       "MonoD13_AAACCTGCATCCCATC-1              Trem2   MonoD13  \n",
       "MonoD13_AAACCTGCATTCCTCG-1              Cxcl1   MonoD13  \n",
       "MonoD13_AAACCTGGTAAATACG-1                DCs   MonoD13  \n",
       "...                                       ...       ...  \n",
       "MonoD7_TTTGTCAGTTCGCGAC-1               Trem2    MonoD7  \n",
       "MonoD7_TTTGTCATCATCACCC-1            MHC_High    MonoD7  \n",
       "MonoD7_TTTGTCATCGATAGAA-1   Ccrl2_Neutrophils    MonoD7  \n",
       "MonoD7_TTTGTCATCTGGAGCC-1               Cxcl1    MonoD7  \n",
       "MonoD7_TTTGTCATCTTGAGGT-1               Cxcl1    MonoD7  \n",
       "\n",
       "[16217 rows x 8 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MonoD28_TGCGCAGTCTTCGGTC-1</th>\n",
       "      <th>MonoD3_ACGGCCAGTAGAAGGA-1</th>\n",
       "      <th>MonoD3_CAAGAAAGTGCGATAG-1</th>\n",
       "      <th>MonoD7_GTCACAACATGTCCTC-1</th>\n",
       "      <th>pseudotime</th>\n",
       "      <th>entropy</th>\n",
       "      <th>ClusterName</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MonoD13_AAACCTGAGCGATCCC-1</th>\n",
       "      <td>0.572615</td>\n",
       "      <td>0.031859</td>\n",
       "      <td>0.166359</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>0.516601</td>\n",
       "      <td>1.065072</td>\n",
       "      <td>MHC_High</td>\n",
       "      <td>MonoD13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD13_AAACCTGAGGCAGTCA-1</th>\n",
       "      <td>0.416252</td>\n",
       "      <td>0.044008</td>\n",
       "      <td>0.229799</td>\n",
       "      <td>0.309942</td>\n",
       "      <td>0.525370</td>\n",
       "      <td>1.203271</td>\n",
       "      <td>Lyve1</td>\n",
       "      <td>MonoD13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD13_AAACCTGCATCCCATC-1</th>\n",
       "      <td>0.305302</td>\n",
       "      <td>0.055104</td>\n",
       "      <td>0.287749</td>\n",
       "      <td>0.351844</td>\n",
       "      <td>0.495535</td>\n",
       "      <td>1.247913</td>\n",
       "      <td>Trem2</td>\n",
       "      <td>MonoD13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD13_AAACCTGCATTCCTCG-1</th>\n",
       "      <td>0.351688</td>\n",
       "      <td>0.050623</td>\n",
       "      <td>0.264344</td>\n",
       "      <td>0.333346</td>\n",
       "      <td>0.481463</td>\n",
       "      <td>1.236459</td>\n",
       "      <td>Cxcl1</td>\n",
       "      <td>MonoD13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD13_AAACCTGGTAAATACG-1</th>\n",
       "      <td>0.453219</td>\n",
       "      <td>0.041082</td>\n",
       "      <td>0.214524</td>\n",
       "      <td>0.291175</td>\n",
       "      <td>0.489453</td>\n",
       "      <td>1.179295</td>\n",
       "      <td>DCs</td>\n",
       "      <td>MonoD13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD7_TTTGTCAGTTCGCGAC-1</th>\n",
       "      <td>0.311247</td>\n",
       "      <td>0.054469</td>\n",
       "      <td>0.284429</td>\n",
       "      <td>0.349856</td>\n",
       "      <td>0.539975</td>\n",
       "      <td>1.246823</td>\n",
       "      <td>Trem2</td>\n",
       "      <td>MonoD7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD7_TTTGTCATCATCACCC-1</th>\n",
       "      <td>0.374501</td>\n",
       "      <td>0.048283</td>\n",
       "      <td>0.252123</td>\n",
       "      <td>0.325092</td>\n",
       "      <td>0.511304</td>\n",
       "      <td>1.226823</td>\n",
       "      <td>MHC_High</td>\n",
       "      <td>MonoD7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD7_TTTGTCATCGATAGAA-1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.999971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.964314</td>\n",
       "      <td>0.000347</td>\n",
       "      <td>Ccrl2_Neutrophils</td>\n",
       "      <td>MonoD7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD7_TTTGTCATCTGGAGCC-1</th>\n",
       "      <td>0.315557</td>\n",
       "      <td>0.054349</td>\n",
       "      <td>0.283802</td>\n",
       "      <td>0.346293</td>\n",
       "      <td>0.494724</td>\n",
       "      <td>1.246926</td>\n",
       "      <td>Cxcl1</td>\n",
       "      <td>MonoD7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonoD7_TTTGTCATCTTGAGGT-1</th>\n",
       "      <td>0.316001</td>\n",
       "      <td>0.054372</td>\n",
       "      <td>0.283921</td>\n",
       "      <td>0.345706</td>\n",
       "      <td>0.504447</td>\n",
       "      <td>1.247032</td>\n",
       "      <td>Cxcl1</td>\n",
       "      <td>MonoD7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16217 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            MonoD28_TGCGCAGTCTTCGGTC-1  \\\n",
       "MonoD13_AAACCTGAGCGATCCC-1                    0.572615   \n",
       "MonoD13_AAACCTGAGGCAGTCA-1                    0.416252   \n",
       "MonoD13_AAACCTGCATCCCATC-1                    0.305302   \n",
       "MonoD13_AAACCTGCATTCCTCG-1                    0.351688   \n",
       "MonoD13_AAACCTGGTAAATACG-1                    0.453219   \n",
       "...                                                ...   \n",
       "MonoD7_TTTGTCAGTTCGCGAC-1                     0.311247   \n",
       "MonoD7_TTTGTCATCATCACCC-1                     0.374501   \n",
       "MonoD7_TTTGTCATCGATAGAA-1                     0.000000   \n",
       "MonoD7_TTTGTCATCTGGAGCC-1                     0.315557   \n",
       "MonoD7_TTTGTCATCTTGAGGT-1                     0.316001   \n",
       "\n",
       "                            MonoD3_ACGGCCAGTAGAAGGA-1  \\\n",
       "MonoD13_AAACCTGAGCGATCCC-1                   0.031859   \n",
       "MonoD13_AAACCTGAGGCAGTCA-1                   0.044008   \n",
       "MonoD13_AAACCTGCATCCCATC-1                   0.055104   \n",
       "MonoD13_AAACCTGCATTCCTCG-1                   0.050623   \n",
       "MonoD13_AAACCTGGTAAATACG-1                   0.041082   \n",
       "...                                               ...   \n",
       "MonoD7_TTTGTCAGTTCGCGAC-1                    0.054469   \n",
       "MonoD7_TTTGTCATCATCACCC-1                    0.048283   \n",
       "MonoD7_TTTGTCATCGATAGAA-1                    0.999971   \n",
       "MonoD7_TTTGTCATCTGGAGCC-1                    0.054349   \n",
       "MonoD7_TTTGTCATCTTGAGGT-1                    0.054372   \n",
       "\n",
       "                            MonoD3_CAAGAAAGTGCGATAG-1  \\\n",
       "MonoD13_AAACCTGAGCGATCCC-1                   0.166359   \n",
       "MonoD13_AAACCTGAGGCAGTCA-1                   0.229799   \n",
       "MonoD13_AAACCTGCATCCCATC-1                   0.287749   \n",
       "MonoD13_AAACCTGCATTCCTCG-1                   0.264344   \n",
       "MonoD13_AAACCTGGTAAATACG-1                   0.214524   \n",
       "...                                               ...   \n",
       "MonoD7_TTTGTCAGTTCGCGAC-1                    0.284429   \n",
       "MonoD7_TTTGTCATCATCACCC-1                    0.252123   \n",
       "MonoD7_TTTGTCATCGATAGAA-1                    0.000000   \n",
       "MonoD7_TTTGTCATCTGGAGCC-1                    0.283802   \n",
       "MonoD7_TTTGTCATCTTGAGGT-1                    0.283921   \n",
       "\n",
       "                            MonoD7_GTCACAACATGTCCTC-1  pseudotime   entropy  \\\n",
       "MonoD13_AAACCTGAGCGATCCC-1                   0.229167    0.516601  1.065072   \n",
       "MonoD13_AAACCTGAGGCAGTCA-1                   0.309942    0.525370  1.203271   \n",
       "MonoD13_AAACCTGCATCCCATC-1                   0.351844    0.495535  1.247913   \n",
       "MonoD13_AAACCTGCATTCCTCG-1                   0.333346    0.481463  1.236459   \n",
       "MonoD13_AAACCTGGTAAATACG-1                   0.291175    0.489453  1.179295   \n",
       "...                                               ...         ...       ...   \n",
       "MonoD7_TTTGTCAGTTCGCGAC-1                    0.349856    0.539975  1.246823   \n",
       "MonoD7_TTTGTCATCATCACCC-1                    0.325092    0.511304  1.226823   \n",
       "MonoD7_TTTGTCATCGATAGAA-1                    0.000000    0.964314  0.000347   \n",
       "MonoD7_TTTGTCATCTGGAGCC-1                    0.346293    0.494724  1.246926   \n",
       "MonoD7_TTTGTCATCTTGAGGT-1                    0.345706    0.504447  1.247032   \n",
       "\n",
       "                                  ClusterName Condition  \n",
       "MonoD13_AAACCTGAGCGATCCC-1           MHC_High   MonoD13  \n",
       "MonoD13_AAACCTGAGGCAGTCA-1              Lyve1   MonoD13  \n",
       "MonoD13_AAACCTGCATCCCATC-1              Trem2   MonoD13  \n",
       "MonoD13_AAACCTGCATTCCTCG-1              Cxcl1   MonoD13  \n",
       "MonoD13_AAACCTGGTAAATACG-1                DCs   MonoD13  \n",
       "...                                       ...       ...  \n",
       "MonoD7_TTTGTCAGTTCGCGAC-1               Trem2    MonoD7  \n",
       "MonoD7_TTTGTCATCATCACCC-1            MHC_High    MonoD7  \n",
       "MonoD7_TTTGTCATCGATAGAA-1   Ccrl2_Neutrophils    MonoD7  \n",
       "MonoD7_TTTGTCATCTGGAGCC-1               Cxcl1    MonoD7  \n",
       "MonoD7_TTTGTCATCTTGAGGT-1               Cxcl1    MonoD7  \n",
       "\n",
       "[16217 rows x 8 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr_res.branch_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "df = pr_res.branch_probs\n",
    "df['pseudotime'] = np.array(pr_res.pseudotime)\n",
    "df['entropy'] = np.array(pr_res.entropy)\n",
    "df['ClusterName'] = meta_data.Cm_Names\n",
    "df['Condition'] = meta_data.condition\n",
    "df['ClusterName'] = df['ClusterName'].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d38d9dc460>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x=\"pseudotime\", y=\"entropy\", hue=\"ClusterName\", data=df, s=10,linewidth=0)\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=1)\n",
    "plt.savefig('pseudotime_entropy_annotation.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d389f8fd00>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x=\"pseudotime\", y=\"entropy\", hue=\"Condition\", data=df, s=10,linewidth=0)\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=1)\n",
    "plt.savefig('pseudotime_entropy_Condition.png', bbox_inches='tight')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cm2 - ACTA1</th>\n",
       "      <th>Cm6 - NPPA/NPPB</th>\n",
       "      <th>Cm3 - MYL7</th>\n",
       "      <th>pseudotime</th>\n",
       "      <th>entropy</th>\n",
       "      <th>ClusterName</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACCTGTCATCGCTC</th>\n",
       "      <td>0.911312</td>\n",
       "      <td>0.088684</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449339</td>\n",
       "      <td>0.299532</td>\n",
       "      <td>Cm2 - ACTA1</td>\n",
       "      <td>Donor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCAAATCA</th>\n",
       "      <td>0.901697</td>\n",
       "      <td>0.098292</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.356300</td>\n",
       "      <td>0.321450</td>\n",
       "      <td>Cm2 - ACTA1</td>\n",
       "      <td>Donor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCAACGGT</th>\n",
       "      <td>0.861729</td>\n",
       "      <td>0.137882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.221263</td>\n",
       "      <td>0.404485</td>\n",
       "      <td>Cm2 - ACTA1</td>\n",
       "      <td>Donor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGAGCCCAATT</th>\n",
       "      <td>0.996486</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.621166</td>\n",
       "      <td>0.023365</td>\n",
       "      <td>Cm6 - NPPA/NPPB</td>\n",
       "      <td>Donor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H_ZC-11-292_AAACGGGCAGGTCCAC</th>\n",
       "      <td>0.980261</td>\n",
       "      <td>0.019739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.554326</td>\n",
       "      <td>0.097032</td>\n",
       "      <td>Cm2 - ACTA1</td>\n",
       "      <td>Donor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-14-173_TTTGGTTAGATCTGCT</th>\n",
       "      <td>0.534815</td>\n",
       "      <td>0.299518</td>\n",
       "      <td>0.165667</td>\n",
       "      <td>0.052382</td>\n",
       "      <td>0.993631</td>\n",
       "      <td>Cm1 - MYH6</td>\n",
       "      <td>Donor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-14-173_TTTGGTTCAAGGACAC</th>\n",
       "      <td>0.459430</td>\n",
       "      <td>0.270208</td>\n",
       "      <td>0.270362</td>\n",
       "      <td>0.030148</td>\n",
       "      <td>1.064546</td>\n",
       "      <td>Cm1 - MYH6</td>\n",
       "      <td>Donor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-14-173_TTTGGTTTCAGGCGAA</th>\n",
       "      <td>0.537538</td>\n",
       "      <td>0.300606</td>\n",
       "      <td>0.161855</td>\n",
       "      <td>0.052644</td>\n",
       "      <td>0.989742</td>\n",
       "      <td>Cm1 - MYH6</td>\n",
       "      <td>Donor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-14-173_TTTGGTTTCCCTAATT</th>\n",
       "      <td>0.454186</td>\n",
       "      <td>0.267055</td>\n",
       "      <td>0.278759</td>\n",
       "      <td>0.025717</td>\n",
       "      <td>1.067147</td>\n",
       "      <td>Cm1 - MYH6</td>\n",
       "      <td>Donor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWCM-14-173_TTTGTCAGTCAAAGCG</th>\n",
       "      <td>0.495281</td>\n",
       "      <td>0.289759</td>\n",
       "      <td>0.214960</td>\n",
       "      <td>0.042849</td>\n",
       "      <td>1.037384</td>\n",
       "      <td>Cm1 - MYH6</td>\n",
       "      <td>Donor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40590 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Cm2 - ACTA1  Cm6 - NPPA/NPPB  Cm3 - MYL7  \\\n",
       "H_ZC-11-292_AAACCTGTCATCGCTC     0.911312         0.088684    0.000000   \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA     0.901697         0.098292    0.000000   \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT     0.861729         0.137882    0.000000   \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT     0.996486         0.000000    0.000000   \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC     0.980261         0.019739    0.000000   \n",
       "...                                   ...              ...         ...   \n",
       "TWCM-14-173_TTTGGTTAGATCTGCT     0.534815         0.299518    0.165667   \n",
       "TWCM-14-173_TTTGGTTCAAGGACAC     0.459430         0.270208    0.270362   \n",
       "TWCM-14-173_TTTGGTTTCAGGCGAA     0.537538         0.300606    0.161855   \n",
       "TWCM-14-173_TTTGGTTTCCCTAATT     0.454186         0.267055    0.278759   \n",
       "TWCM-14-173_TTTGTCAGTCAAAGCG     0.495281         0.289759    0.214960   \n",
       "\n",
       "                              pseudotime   entropy      ClusterName Condition  \n",
       "H_ZC-11-292_AAACCTGTCATCGCTC    0.449339  0.299532      Cm2 - ACTA1     Donor  \n",
       "H_ZC-11-292_AAACGGGAGCAAATCA    0.356300  0.321450      Cm2 - ACTA1     Donor  \n",
       "H_ZC-11-292_AAACGGGAGCAACGGT    0.221263  0.404485      Cm2 - ACTA1     Donor  \n",
       "H_ZC-11-292_AAACGGGAGCCCAATT    0.621166  0.023365  Cm6 - NPPA/NPPB     Donor  \n",
       "H_ZC-11-292_AAACGGGCAGGTCCAC    0.554326  0.097032      Cm2 - ACTA1     Donor  \n",
       "...                                  ...       ...              ...       ...  \n",
       "TWCM-14-173_TTTGGTTAGATCTGCT    0.052382  0.993631       Cm1 - MYH6     Donor  \n",
       "TWCM-14-173_TTTGGTTCAAGGACAC    0.030148  1.064546       Cm1 - MYH6     Donor  \n",
       "TWCM-14-173_TTTGGTTTCAGGCGAA    0.052644  0.989742       Cm1 - MYH6     Donor  \n",
       "TWCM-14-173_TTTGGTTTCCCTAATT    0.025717  1.067147       Cm1 - MYH6     Donor  \n",
       "TWCM-14-173_TTTGTCAGTCAAAGCG    0.042849  1.037384       Cm1 - MYH6     Donor  \n",
       "\n",
       "[40590 rows x 7 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Donor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d38dbb5880>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_DCM = df[df[\"Condition\"]==\"DCM\"]\n",
    "ax = sns.scatterplot(x=\"pseudotime\", y=\"entropy\", hue=\"ClusterName\", data=df_DCM, s=10,linewidth=0)\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=1)\n",
    "plt.savefig('pseudotime_entropy_DCM.png', bbox_inches='tight')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x=\"pseudotime\", y=\"entropy\", hue=\"MonoD7_GTCACAACATGTCCTC-1\", data=df, s=10,linewidth=0)\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=1)\n",
    "plt.savefig('pseudotime_entropy_BR4_prob.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_projections.to_csv('pca_projections.csv', index=True)\n",
    "ms_data.to_csv('ms_data.csv', index=True)\n",
    "tsne.to_csv('tsne.csv', index=True)\n",
    "df.to_csv('palantir_meta_data.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdl.to_csv('fdl.csv',index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/jamrute/Documents/Graduate_School/Thesis_Lab/Lavine_Projects/Andrew_HDCM/Palantir/myeloid_single_cell/myeloid_palantir_meta_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cm4 - ADGRL3')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cm6 - NPPA/NPPB')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cm7 - BMPR1B')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cm2 - ACTA1')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cm1 - MYH6')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cm3 - MYL7')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zk/Dqp+XI+Kk1qscrotkhYGvZadw0YwASLTpMOTcbL981GWMGB/po5JnZoXYN5MgmCCJWxMRHMXr0aHzzzTfS61mzZmHWrFnS66ysLDz33HOxGFrYFPZOQoPdjYtHZksd7r4/Wo/6ZgEHjzcgK9WEvhlWPH3zKCQnWKDTtiiBbw+exqbvKnFOQRouKu0j7SJYqKwcjuNgMBjg8/nI9EQQRMwg6dNOCvskItnS0hJVBLDyg/3ITrNgVHE6FkwvwjWT8mHQ8bAYdXA4nXjp33txpsmJL3ZVItnC470tFWi0uQEgZCtUjuMoNJYgiJgSd1FPXYVJI/ri/ad+hAgOTXY3cntZkZKgh+Ynga7RtDih7S4vLEYtLirtg0+2VeBX01uc8ldf2B8c54NWq4coigG7CYIgiHiBpFM76ZVshtWsQ32zC/OmFkLLc7A7PbCadNI5HMehpsEJi9GKZKsB5+Snyu7wUzitzwe9Xh/l0RMEQYQPmZ46gM0hoG+6GVqeA6/hwIGDf1sJx08hsRyAvEyrdJxZkqgPBUEQ8Q4pig5wqt6FjTtPYufhWvh8Iqpq7fB3JRRlJ6hf/BPUtIggiHiHTE8dxOsT8c6Wo0g063CyzoHCPgkKxzPPh9bFHo8HHMdBp9OFPI8gCCJW0I6iA/TvnYBEsw4XDs9EryQjKk/b4XAGlk+X7xfUIpyotDhBEPEMKYoOcPfVpcjNMMPh9uJ/P1RjeF4y3t92POQ1Lcl1Z19zHEcRTwRBxDUkoTpAQZ9E6HUaHKxsKQBYedqOZoeAqy/MV5wXmAEhSkcNBgMl0xEEEdeQhOogZxpdEEUR5xamYnBeMnRaHi6V4n/BoEQ6giDiHdpRdJAFlxaj4ngThuQlwycCCSYd/vTKd1i6YERYSoAUBUEQ8Q7tKDrI0Sob0hINSE8yQKMBmhwCjtTYAlqkMkKV6yAIgohHSFF0kNzMBGSmmNBoE2DQafDBtuPQ8hr8e8eJgHObHYERUQRBEPEOKYoOUjqgl1Tcz+n2ojQ/BY12DzbuPBlwrkHHBxyj0FiCIOId8lF0EFEU8a///Yjh/VIgApg5NgdHTtlwotYuNR1isFLjcgRBgMFgiOKICYIg2gYpig7C8xocONaIPRX10HAcTtQ6cKbJBZfbi2/3n8KIgb0V5/srD6/XC6/XC54P3G0QBEHEA2R6igB5mVb0SjYiI9kIh9sLjgN8IrDs7f2K80RRhMPtCajtRLWeCIKIZ0hRRIARhakY1i8FVqMW6Ql6zJ9agIIsKwx65cfLcRzUXBJqvbIJgiDiBTI9RYBvDp5BVqoZdpcXW8tOI693Am6eNQh2tyfgXLMh0MREioIgiHiGFEUE8HlFfHPwDFyCF1aTDk0ON4x6HiaDFqIIRelxjuNkBTzOQn4KgiDiFTI9RYCkRCOMOg0STFpkp5mQaNRBp9Mgr3ciNJrA5Dq1dDuXy9X5AyUIgmgHpCgiwP9dMhCchkNGsgkThmWipsGJ5W/shtfrhdFoDDg/WGa20+kkxzZBEHEHKYoIYDLoUNfkRnWdA5/vOYXaRhcqax0AAkt2hCrf4fP54HA4SFkQBBFXkI8iAiRb9Ug0a5GflYgbphUi2aqD2aiDTqeTFENb6js5nU6YTKbOGi5BEESboB1FBDDqtRg/JBPTzu2DZKsOBi2PS8f0xfc/1qGuucX30NKwqOWfx+tDfbM76P1EUYTH46HyHgRBxAW0o4gQX/5QjU++rcKcif0xqjgNH31TiR+ONsFq0uHOXw5WhMVyELHivTL0Sbdg7pR86FVKe3g8LaG1BoOBqs0SBBFTorqj2LdvH2bPno2SkhLMnDkTu3fvVj2vvLwc8+bNw8iRIzFx4kQ888wz0Rxmu6hucMHu8uKFTw7hw6+O4bvDtbA7BTTYXGjw2z1oNBrc+LMByEwxouxYk+r9fD6ftAPxer3weAIzugmCIKJB1BSF2+3GokWLMH36dHz99ddYuHAhFixYgObm5oBzf//732PcuHHYvn07XnrpJbzyyivYuHFjtIbaLuSL/i0/1OB4jR2n6h04XmPDY+/8EHB+7xQjhuQloygnJeg9RVGE0+mEy+WCIAiUmEcQREyImqLYvn07BEHA/PnzodPpMGPGDBQWFmLDhg0B51ZUVAA4W0CP47i4r7A6cXiW9HO9TYBep4HHK0IUgWPVtgB/g0bDIb9PMtISjTCZTGGZl2hHQRBELIiaojh06BAKCgoUx/Lz83HgwIGAcxctWoQVK1Zg2LBhmDZtGi699FKMHz8+WkNtFzddOkh54Ceh7hNFJJh1qo5pDj6FiSkYHMeB53loteRSIggi+kRN8tjt9oDkM5PJBIfDEXAux3G46667MHv2bFRUVGDRokUoLCzEL3/5y7CeVVZW1q4xOp3Odl8bcC+hRfCX9rfgktJUOJ1OWK1WxTmVNU0waz3Q6XQwGo2KXYUoihAEARzHoba2ttMytyM5564CzblnQHOOHFFTFGazOUDYORwOmM1mxbE9e/ZgzZo12LJlCwBg4MCBWLBgAdauXRu2ohg0aFDrJ6lQVlbW7mtbOKx4ldvLgruuG4sUqwF2ux0OlwCj/uxH7vUBKSktPgr/HYfc3Na7d29wHAetVgutVhvRKKiOz7nrQXPuGdCc28aOHTuCvhc101NBQYHke2CUl5ejsLBQcezkyZMQBEFhimECMt4ZnJuERLMOGUkGnFeUiqsmFiLBpAPQMoeq03bF+Ua9BhzHQafTBdzLXxmw6CdyaBMEEW2ipihGjx4NURSxZs0aCIKA9evXY//+/Zg6darivHPPPRc+nw9PPvkkPB4Pjhw5ghdeeAEzZsyI1lDbTX5vK7LTzMjrZUFOugWrN5Rh/bajAFoURVaaRXF+WqJREvx6vV7xHs/zqjsHyqkgCCLaRE1R6PV6rF69Gh9//DFGjRqFVatWYeXKlUhNTcW6detQWloKAEhLS8Pq1auxbds2jBkzBjfeeCOuvPJKzJ07N1pDbTf/3lGJg5WN2FVRj093nkCjzY1TdS27CI1GA7Mx0Gzk84lwu93QaJRfhUajkZSFVquFXq+HXq+nUuQEQUSdqNpzioqKsHbt2oDjs2bNwqxZs6TXJSUleP3116M5tIig17b0oAAAn8cLEcDXZTW4qLQRfdJbfDH+0U0+UYRGJeKJJdgxk5O8bhRBEEQ0oVpPEeTcwlRoOCDFqsfQ/FQAwJFTTfjzKzvwyBs7A84XRREiRNX2qCxsliAIItbEv4e4C1Fxshk6rQZNDgHa+pakOp9PRF2zC+Y6Ldw+DjrurPDnOA48OPA8p+qkZjsIf7MUQRBENCEJFEEabG6crHWgpt4Jntdg6rlZ4DUceA2HqSP6ItkamIHtcAk4WeeAzeEOiOwSRREGgwF6vZ7MTgRBxAxSFBFk+uhcAICW10AUgdQEPbw+ESkJBlwxvr/qNWajDmcanNjyfXXQKCdSEgRBxBJSFBHk/y4ZjIE5ScjvbcWU0iw02ltKhdc2ubDvaB0AqNZ1evydH8BzZ0uLyxEEofMHThAEEQJSFBFEo+FQ1DcJYwdnYPOek/h890kAAK/h8OK/9wNQz4P4xQV5mFSSFXAcoEKABEHEHnJmR5jsDCtWfVAGQIT3p2gmZn4KxrQRfaRQWI7jFMqB8iYIgog1tKOIMCOLM6DXasABYHuHkQPS8NvLhoS8zieKQbOxGa1VmSUIgugMSFFEmD6pFvTrbYEIDnqdBrwGSE0wIsGsD3nd0rW7cKLOFVD3ifktRFGEy+WCy+VS9WUQBEF0FqQoIoxGw+HEGQcsRi083hbz0+GTLe1Oy6saUXXGprpzOG9AGr7eVxNgavL5fHC5XIo8CyoMSBBENCFFEWE4joPN6UGjXYDXJ0KrARKMWrzzRTkee3sXlrz2LY6edgZcNzgvCf/dXaV6T6/XK/kvAHSJSroEQXQfSOJ0AkadBoV9LLC7PLA5PDhQ2YCDlY2wmnXQgMPxGhuyU5IV12SmmFHbFLw5kUajgU6nUygMgiCIaECKohMYnp+GQycaUNckwOP1ISPZCLNRi7xeViRbDRgzqBcMOo2iux+v4TD7wpZWsTqdTpE/odVqpTIeciXh8Xjg9Xrjpk0qM5OJogi9Xt+uMfl8Png8Hmg0GtXrRbGl2i7Q8jm1pbyJ/86sreMSRREajUaKTGP/Z6bA1oIR/JH3GGFVghnB5sUCGqisCxFNYi9duiENNhdO1Z3dHdQ2unD7z8/ByOIMxXlyocVxHC4+Vz2XQi07WxRFyant8XjaLKQijSAIUsVb9lou+FhrV/Gn6C65gGW7JY7jpHN8Ph80Go0kEL1er6KiLsdx8Hg80Ov18Hg88Pl8CoUKtAh3dh2D4zgkJiaGnAt7PhubXDmx+/t8PukzlwccMKEfTtkVn88nzZfjOEWXQ51Op+qvYuPoKs28iO4B/aZ1AvuPNyheazRcgJJQQy5o5QiCIPWnYDDlEQ+mKLnQZ/iveOXClwlWuWCUC161z0CuJOTPYDsQoOVzYu1jPR5PQKdEoOVzC7YaZ/diuwd2rXxMbMwc11LIUS6s2WfAPo+OCHK1z8D/8yKIaEGKohMQ/IKSUiw62JwCLEZl6CtbrZ59zcHlckGv1yt2DADgcrWEzsrDZ/V6vbTyjrWykO+M1Fa78vExAS8XwOx9nU4Hr9er2E2wa+Srdfl9gLO7M7XVuXwMPM+joaEBvXv3VrzHdg1quSpqQlsURWi12oBVv1wJulwu6TsWRTHAVMbzPHQ6nfRZsDmy9/zheV56n3YTRDSh37ZOgAMgFy3VDS6sfP973Dm7pNVrm+wuJPNa6HQ6xQ5DTfgxwRdrmPBmQjpYcUODwaDYAcnt7f6Kxh8mlP3vz8xWbrdbYZphwpc9h91bFEUkJCRIire9tn4m5NnY5MflpiqmONgOxP95/nMN9X2yz5Agog15xDqBCX51m3wi8FXZKew/Vq84rlYg0GzQ4v2tP4LjOBytscEleOFwe1F5xgYgfms/sRVxqJ2N/Bz2c7i+lVD3Z/djioApI41GA5PJBLPZLAlgr9cLs9ks+Uzk99Dr9dIz2NgMBgN4ngfP8zAajdJuyb/HudpY5K/ZzwTRFSFF0QnoNRx6JRuRmWyExdiyYmyyC1j72SH4fMrGRWpsL6sGAFiNWui1PIx6HmkJBni9XqomqwLbWYWKHJJ/1kzZqvUpZ8oCgKQgDAYDDAaD9H5rjmqmdHQ6ndRPRB4FxnY+lGFPdBVIUXQCbsEDvVYDnVaDPmkmAC3mqFO1dtQ2BSbbyRFFEQ12F47XNMNi1IHjWq416s8KGUIJx3GSUNZqtZJglysN5t8xGo2oqamBTqdTNXExIe9/PXA2SikcAS9XXv7+FmYmY45zgoh3wlYUc+fOxdtvv43GxsbOHE+3YPzQLAztl4w+qSbotS0fcYJJh7zMBCRblTZmfxMGx3Ew6rRY+vp3ePHjg9Ixhn8tqHiC2eZdLldMBaCan0QuuFnuSbBdQTA/iyAIUrhtR+YnvzeZo4iuQNiKYvTo0XjxxRcxfvx43Hzzzfjkk08khx2hxGjS4z/fVuHrg2fw/Y8tobKJFh0mDM+Clg90Zvr7HXTalg55dpcHgkcpkDrbec2iddRCS8O5loWWsmTA7rRijpRQZ2YoanFLdBXCVhQ333wzNmzYgLVr1yInJwdLly7F+eefj8WLF+Orr77qzDF2ObaXnYLXB8jl7PEaO17beBB2Z6DZwt9Wfv2UfAzItuLXlxRBpw3MR+hM2GpZEAQpy9oflifAlIAgCHA6nVLmM0t0c7lcqK5twmNvfodX/3Mgbh3x4cLMVR2JlgLORnZRdjXRVWjzb+qQIUNw1113Yf369Zg7dy4+/PBD3HDDDZg4cSJWrVoFlyt4vaKewtRz+wIALEYtzIaWHYAI4GStA053oKLwt4XnZyVg4YxiWE26ADNFZ69A5bkNzJQk312waCFBEOB2u6USFMDZEhnsPADw+UQkW/UoP9GIM/XNcDqd8Hg8Usn0rrLj8Hg8Up6F/64uHJMbU7zs/1QynuhKtCmPwu1247///S/Wr1+PzZs3IykpCddccw1mzpyJ6upq/PWvf8W3336LZ599trPG2yUY3C8V107uj8/3nAIAJHtFVNU64BI8+GxXFS4/vz+aHQKSLC3+Cb1eHxCqqYa8tEVnwUxhTPgzUxKz8TPlwJSWPHFOnlzGFIXXJ+JgZROumdQPRr1G2q2wOarlFrQGM3FFs2wJE+jMvyEfs9xnoTYftsMCAnugs50ZK2ECnC3PIt+ByXcg8tBe+XXyqDi1EiAE0V7CVhR33nknNm3aBJ/Ph2nTpuHpp5/G2LFjpV/SwYMHw+Fw4N577+20wXYlzi1Mx56KetQ1u5CTYUZVrQNOtw/rvvwRH399DD6fiKkj+mL2pMKgZTjkmbiM9gjWtsCifuQF/thxeY4CcDZbmhUBZIqCCVWNRoM+GUYsumwIMhK1ki1OnpHe1rnIk9m8Xm/UEtDk2eT+35U8o1ptPv67Qn8THPtO2c6S7dLkCYn+5UnkioldJz8uCAIpCiJihK0o6uvr8ac//QlTpkyB0WhUPWfYsGFYvXp1xAbXlWl2+jCqOA1urw8bvz0hHXe5vThxxgadlsen31Zi9qTCoPdQa1AULTs/ezbbSbCMYyYw5U54trNgK3z/SK68zARJgbD7dURRxAJ5iXd/RSHf2QRTFAaDQdoJybO11e6plv8RLFIqnJ8JoqOErSiYOclms2Hv3r3geR79+/dXKI2+ffuib9++Qe+xb98+PPDAA9i/fz9ycnKwZMkSDB8+POC85uZm/OUvf8GmTZvAcRwuvvhi3H///XEdGuqPzSXguX8fUhzjOACcCKOeh9cH9E79KceC42A0GuF0hs6xAM6aQOQmh85AngXNVqb+SkBuUmlt9cpyHeSvGW0pnc1W3kxZRYvW/EOtjV2ucP2Vrn+hQpbY518AkaHVagO+G0D5O0G1oIhIEvZyzu12495778WYMWPwi1/8AldccQXGjBmDJUuWhNWa0+12Y9GiRZg+fTq+/vprLFy4EAsWLEBzc3PAuffeey8aGxuxadMmbNiwAXv37sXzzz/ftpnFmC17TgYc6987Af/4fxMwqSQb6UkGnKpz4JNvjgNQr7aqdhw4a6PvTHielzKKgykBjUajyFpuD6wXOHOahwMLLe2qUUPy0iX+/g7/c9g/tV2M//fClHFnLyKInkfYy46//OUv2L59Ox5//HEMHz4coihi165dWL58OfR6Pf7whz+EvH779u0QBAHz588HAMyYMQOvvvoqNmzYgKuuuko6r7q6Gps2bcLnn38Oq9UKq9WKp59+usv1ie6VYgo4Jnh8cHl8uHx8PxysbMmv2Ly7CtNGKndh8sihYAohVGMb5gjtaAhmONd2VCDJV81dJQKKIHoaYUuRjz76CMuWLcOUKVPQq1cvZGZmYtq0aVi6dCneeeedVq8/dOgQCgoKFMfy8/Nx4MABxbGysjJkZWVh3bp1uOiiizBx4kS89tpryMzMDHeoccHE4b0DjtU1OWFzCOiTZkFWmhkAMLIosJmRHDXhGWrFyMxBLLooHFgUDSstEU3kZhcylxBEfBL2X6bBYFD1ESQkJIR1vd1uD3CCm0wmRTtQoMVpXllZiYMHD2LdunWora3FwoULYbFYsHDhwrCeVVZWFtZ5/jidznZf68+3hwJLndhdXmzfdQDD8yy4otQEp2CAxXD2mTzPIzs7O+A6ufJgiWx1dXWqOStarRbp6enSfOrr60OO0+l04scff0RSUlLY13R1Ivk9dxVozj2Dzppz2Irid7/7He677z48+OCDKCkpgUajwYEDB/Dggw9i4cKFCoFvMgWaXcxmc4BgczgcMJvNimN6vR5erxd33303LBYLLBYL5s+fj7Vr14atKAYNGhTutBSUlZW1+1p/mrlq4PMa6XVuhhmn6p04b3gR8vsEb8Vpt9sBKHsoyHcPrMx1VlZW0MJ2LPchOTkZWVnq7VUZZWVlyM3NlUJOrVZrq9d0dSL5PYeDvEVqrEJWoz3neIDm3DZ27NgR9L2wFcXSpUtht9tx7bXXKnoFi6KI3bt3Y9myZYrB+lNQUIA1a9YojpWXl+Pyyy9XHMvPzwcANDY2SruVruafAACTUSkQTjc6kWzRI79PIpodAt7dUgGDjsfl4/tBrw3M9JVjcwoQfSIsppYdHVMcbfVfBEMeZdNVHcSdBSsJzhzF8t97Fn3EMtflUUxy86AgCIoWqvQZE12NsBXFypUrO/Sg0aNHQxRFrFmzBtdeey0++eQT7N+/H1OnTlWcV1xcjKFDh+Lhhx/GI488grq6OqxZs0bh8O4KNNmV/gG7ywe7y4mvfjiJw1WN2PpDS9Z2WpIRF5WeNTeZzWY0NzcrdhFmg/JrYrH3kbTpk/BSh4Ujs8ACVn0WaFEALFSXnctyI1heiT9dod5VqD7s/q1riZ5B2JJm1KhRAFpsYEeOHIEoisjJyYHVag3rer1ej9WrV+OBBx7A3//+d/Tt2xcrV65Eamoq1q1bhwceeADfffcdAGD16tVYsmQJpkyZAp/Ph5///Oe4/vrr2zG92DGyqBf0PAe3V8TIAWno39uKzXtOYdkbu2A1aqHRcLCadEg0B/p9/Htpq0Er0+jA87ykCPw/b7VEObWwZtbWNpjpiQUTqPXVDnWuWs/ujsICGtgu0//ZrN4VlQjpWYStKLxeL/72t7/h5ZdflrbeOp0OV1xxBf74xz+GtbotKirC2rVrA47PmjULs2bNkl6npqbir3/9a7hDi0t0Wg2GF6bieLUNl5+fA5Nei5wMC5549wdYjFr0zbDgqgsLUFKQHnAtM2EAaitQDhxHoaTRQp7nwJSC3EzHsq7ZKlwt09o/2dAfeSVej8cTtM0qoMyh8Xg8ERXW8nBseY0v+bOlGl4/1bwiegZhL0kff/xxrFu3DsuXL8d///tfbN68GcuXL8fnn3/eYbNUd+Xy8/uj3ubGqg8PYNPOE3B5fPCJwKk6B4b1S1VVEgCClkgBfsru/gm1UFrqmhZ51HYO8iQ4eT/v1vqGq+GflR2pc9sCU0DsnsGSABmkJHoWYe8o3nvvPSxZsgQTJ06Ujl1yySWwWCz44x//iNtuu61TBtiV6ZeZCC2vwZkmF745cAZbvj8bBXWmKXQ5drPZLEVAMfxXeIIgKFaf8kZSBoOB7MhdBJbhHk4wgf8Opq3IKxDLq9Wy3x12f7V7s/fYz0TPIewlid1uR25ubsDxnJwc1NXVRXRQ3YWMZBPyMq1IseoxbnAvXDY2BwCg5TmMHdSr1etZBzT2z1+IyF/Ls7m7gsOUUKL2/co7Baqd2x5hzUqlyCsDy39fWlNA0eiJQsQfYSuKYcOG4bXXXgs4/tprr2HIkCERHVR3gddwGDEgHf/visEYP7QXcjJackay08wYUdy6omjN7yPPpPZ3lHaGspA3LSJl1PkIggCPx6MIr+0I8l4hbGfB7svKvXSlwptE9Ajb9PT73/8e8+bNw/bt21FSUgIA2LlzJyorK/Hcc8911vi6PDqex+bdJ5Fk0eG7wy07L7NRh8rTNmSnWzp8f6fTCYPBoKjwCqBTGhzJV7eRDs9tL2qmlJ4K81HJK+zKnc7sO2OK3uVySTsE1sebINRo047i3Xffxfnnn48TJ06grq4OkyZNwkcffYTS0tLOHGOXxmrisX57JV7/7AjKjrYUAqxrduGRN3airhU/BYCAzHU12KqTRaN1VuhsPApitsMJ1t+7K8PCX8Mt7sh2mEyhs26Ect+VWhtXgKLoiNCEvYT4zW9+gzvvvBN33XVXZ46n2yEiULhaTToIXh+aHAJSElrv0KbVakP2Vpb3rWarw84Q6vKVabxEvbTFvh5L5DkILIyW9aVgrVB5npeEPFMSbVH48u55wT4HtoiQt7NlUVwEEYywfwt37txJ9st2MHNsv4BjzXYBl43rh9xe4ScrhkIURQgeLwSPD16vr1OEpdfrleL240mosDIa0eyfHQwm9P3t/wAUrU3leRD+P7NdYbiVf+Uw8xHr1cF6ifj//hgMBphMJphMJhiNxk5bWBDdh7B3FPPnz8edd96J66+/Hjk5OQGx/oWFwVt69mQ4jgMHQG4Usbs8uPi8nA7dV56UBwBaXoO6Jid8IgeLxSw9uz34r0rlJct9Pl/U+lSHQ6QUl7zXt7yDXLjITX/A2eggtlNQa28qz+T2V3Tt+e78/UYajSbojoQUA9EWwlYUf//73wG07CwY8kzUnlbOty0kJxgU/oiSgrQO39NfUQBASoIRHq8PDodDWmW3tduZ1+uVlIJaF7nuKmDk5jsAbd49+4ewsr8LVvNJnish/7uRV5aV99am8ixEPBG2oti4cWNnjqNbM35IJjZsP4Y5F/ZDr2QThvdPlcw47RW8TJiwQnVsZar7qRItWyHLwx+BsytdVnsIgCSomO3a/xksbDLafaqjSUcVIPMvMFgUlny3I89BYP93uVzSd8RayrJoJWppSsQLYf/V33PPPXjqqaeQmKjspVBbW4tf/epXYXW566mkJxkxODcJ5xa27CQ0nNLM0R6YEOd5XhL0wc4DWkwj8kxcplgyMzMVOwhWBM9fyMWbbyLSsLkxJ3NbYb1B5LuF1vBPjmT+DaasPR5Pqzsb5iT3er3SroWZCpmCJ2VDdJSQUmrbtm04dOgQAODrr7/Gm2++GRCuWV5ejqNHj3beCLsBub2sWL/tKDxeH7S8BrVNLqQlBTZ3CoXBYAho/MSaGPmX+mgNuXCSKyomJP2L4HV3mIDuaC6G/24hnPPZLk6e++B/v1DIFQtzoLNFCHvdnRU8ER1CKork5GS88MIL0srntddeC3DKmc1mCplthaH9U1Fd78Tyt/YiNcGAqppmvPPQxW36A1Y7l4VRqtWFksNCZuWlrtkuoqGhAb169VI4PnuKggDONiYCIPl0ool/3Sb59xyOn4KZs+T5M3ITIvk6iEgQUlEUFxdLvom5c+fiqaeeknorE+FjNrQIn9omN2qbWoTS258fwdWTOxYpxspTazQamM1mKfnMH5aFC5yNjDEajZLjOp6imMKFhZDKcxDCucbfUSwvnc0+O7nvBwiMSIo0/vduywKC4zgYjUZpvPJIK6rLRESKsA3kr7zyCoCWPyYWEy5HrU820YJBz0PDAT7ZR/bSx/vRr3cCxg7OjNhzWGJeqAxlZqpgK86UlJQ2JarJcwVimbsQbl8GuQBlCW/p6enSPORZywACFC3raBfPylQtAZJ2EkQkCVtR7Nq1C/fffz8OHjyoOE7hseExc0xfvL/1ODQcMOmcLOi1HL7eV90mRcFMDHJF4HQ6Jb8RW12ykh7BYA7r9lSaZYl3jFhFQckVVDAlwXZYzPTm333OP2IsGPFYGkQeVttWHwRbKMgjrFgggxzmFKeOdkTYf+VLlixBQkICVq5cGXb7U+Isvp+S4M8f0guXjMqGlufAcW37wzMajXA4HIpjoijCbrdLGbnM9KDVauF0OlWFnNxPwZRFe3sbxIrWejjInbzMTMXyGuQ9rdnumNn3/SO+2hsF1VmweflnfrdljGwRIbcMuFyugEAVZj0AWupIkdWg5xK2ojhw4ADefPNNFBcXd+Z4ui0lBan4YOtReH+yP6UlGpCW1LbqsaGEOVvls3INHMfBZDJBFMUAheF2u6WVNBOO4ZoqWH0ioOO7CeYjCSeMUy7Q2djl42bCk5mY/JGXY2d5JAaDAUajUZFN3VqtpFjDzGdqYw4XtWtam2+8fh5EdAj7Lz0/Px/V1dWkKNrJuCFZAHZia1kNOI5DslmLX88chrYG2ZhMJthsdrC/W/kfsFotJmaOUtuJMNpaeK49kUEsAZCZMZiwBtT7MzP8fWJqAk6n0ynuFQ5qJTX8f45H5PNnPqK27nj0er3C9BQskZJFyrHvjOi5hK0o5s6di/vvvx/XXXcd+vXrF/CLI2+RSgSi0/LgNYDXB/zv+2oAwJlmAX+5cXSb7tMSkmyC0+lUfZ9FN8nNCKw0hH8eBtCSMJmUlCStwjsrQYvZwoGzLVzlYZzyZ/onnbXmnJe/H2qFzfJOqqqqkJyc3NEpxQSdTqfIu2gP8ppQoRYJ7V0UEN2PNmVmA8Bjjz0W8B45s8MjNcGAmoazwnrf0fp23YeFw4bKnbDb7YqGRsFWnW63Wyr1wQQ0MwWFu9OQXxdu7D9wtt+C3BwkVwzhmlTkzmn/Sqnypkbsucz0Fq/II7X8YZ9xPI+f6H6ErSj27dsHADh48CCOHDmCcePGoba2FtnZ2RSKFyZpforC4Q7eYyIcWlMWbAfByk+bzWY4nU6FeSY1NTVA6KjlG4SC2c2BlgQyNSGmlkgmD1v1d86Ggjmh5c+XP5OZVNguKZwy2vHim5A7kNlnKW9fyhzRalFI8TIHovsRtoRvbm7Gr3/9a8ycORO33XYbamtr8fDDD+Pyyy/HqVOnOnOM3YZZY/Mifk+z2dxqNAqrBQQgIB/AarVKiWv+vZQ9Hg9cLlervRH8mwcFg/lP5IKMledWUxLBBJ5/2W615/sXNwyFz+eDy+WCy+VSRErFouubfw8Ll8sFp9MZ8D34j00+B+pWR0SasBXFsmXL4HK5sHnzZknY3HfffUhISMCSJUs6bYDdidLiXorXLiEy4aWslEooheFyuWC326WYeUDZI8Mfj8cjKRgWURQM5m8It2WnHH//Amu0YzKZAsqcM7MLa8rDTDAsB4CZsti58utCIZ8bU5JOp1P6F80wYLnvQJ7cCCiVl/9uQq4Y/UueE0RHCdv09N///hf/+Mc/kJl5NkGsT58+uP/++zFv3rxOGVx3I6ONhQDbClMYocxRzBxzuKoB/XsnguMAUQRCWSuY4GTOTSa8mB+DCe7WYJFPTCH4J++xMtsM9jMTmkwRseP+zbPktKW4IVM4LGfCXzizmlrRQB61Ji8FLx+rmv9Ibo6T54CozV1+vC0hykTPJezln91uV/3DbG21KWffvn2YPXs2SkpKMHPmTOzevTvk+YIg4Morr8SKFSvCHWbco/H7O1z8wnYsff1bNNgCY//bi9lsDkie8ic/KxE/VjehrskFh6vl2XJbuBqspDXr6+x0OmG32+F0OiUTEnvf7XZLphy32w1BEOBwOCAIAtxuN5xOp2qzHzny0E8WsRSsTpMa4dY6YoqOCUp/01aohL7O3G2wJEqDwSCNTR5m7D8Hg8Eg+TWYGcq/1zozJzK/kn+lWYJQI2xFccEFF2DFihUKO2ltbS2WL1+OcePGtXq92+3GokWLMH36dHz99ddYuHAhFixYgObm5qDXPPHEE90umkrLKz/y6joHjtfY8M2Bmog/qzVl0S8zASkJBpgMug4JPFbqQa4kmDJhyiJYBzj/Y/7odDoYjcaAME25MuqogGMKkM0BgCR0/Xc57HzmN+hs05Tc3Cb3IfkrAEBZSVbNVwMgIHfFXyGyBYDL5VKdF1Mu4X7mchMm0XUJW1Hcf//9OHnyJEaPHg2n04kbbrgBF154IZqbm7F48eJWr9++fTsEQcD8+fOh0+kwY8YMFBYWYsOGDarnb9u2DV9++SXGjx8f/my6AAVZyvInDrcXOq0GBVkJnfK8YLuLaCaZBXMMy4VdWxPH/P0KHcU/JFdtZyF3GPubpjobuWmIlSIJhnwn5f+ZstfySrNs1yJPggz2nckXA+HUyGJKpbWACCK+Cdvwmp6ejjfeeANfffUVDh8+DI/Hg4KCApx//vlhCZpDhw6hoKBAcSw/Px8HDhwIOLehoQH3338/nnrqKTzxxBPhDrFL4PYo/7iunzoAH3z1I575oAy3XTkUfdLaVtYjXOTKgvkw2lr+gQkrtcS9jqDRaEL6G9TQarVS/aaO1mJiCWgejyek45uF8bJr5DWiogErn8JKqweDOfjZz3JYHTD5e/Lxy1u6qu2kgu0y5PkqwcZEdF3a7KEbM2YMxowZ0+YHqfk4TCZTQGkJAHjggQdwzTXXoKioqM3PiXdyM604fKJJev3NgRp4vCKa7G58d+h0pykKOUxpiKKIiooK9O7dO6w/ZLaiBiBFJwEICK9tK2zFyQQYS95jY2IJaP5mkkiW/mZFEuU9PlhpCzVHttyBHy0hGCxTWi1/ItSYQr0njxzzP4893+v1SmHOzGynlkfDFBZ12ev6RK1GtNlsDliJOhyOALPIO++8g7q6Olx//fXtflZ7/RpOp7PTfSKFaT58Jnv9Q3k1jAYevIaD2VePsrK2tTXtKMweLScpKQlWqzXkHzdzilZVVYVUDkajERaLRVp1WiwW1fu63W7JrMEUAisTzgTy6dOnVW3zbUXte+Z5HllZWQqHMdAihFmekEajkXrGNzY2xtz5azabYbFYoNVqIYoimpqaYLPZVM/tzN/t3r17Sz8fOXIkbsqyR+PvOd7orDlHTVEUFBRgzZo1imPl5eW4/PLLFcfWr1+P3bt347zzzgPQoky++OIL7N27F//4xz/CetagQYPaNcaysrJ2XxsuRUU+rN541i+j1erx5G0ToOE4GHTRX3WFmjMT3MHQarXIzc2VXstLnANnBT17z2AwKDKP1ZCvmHmeV+xC+/XrF+A3aA/B5uxwOCQhJy99UlRUFLCjycrKkn4Ox/QSaURRlHwlzARmNpuD7rI683db3t8iJSWlU57RHqLx9xxvdGTOO3bsCPpe1BTF6NGjIYoi1qxZg2uvvRaffPIJ9u/fj6lTpyrOe/755xWvFy1ahEGDBuGWW26J1lA7FZ7XQM9zcHt/6gPg9cCkj03zn9bQ6/VSpdFw/BLMDKFW5jtYFE5bYEomWJmQSMNxnCJiS147Sw4zvTBTS1vHxqKYmK8k3OvlfhIgdk2k/CsWE92PqBVp0uv1WL16NT7++GOMGjUKq1atwsqVK5Gamop169ahtLQ0WkOJORbT2VVzkz3+wwZ5nu+wPyCYIGlrddL2+kHac1//12q7K/l17R0Xy0FpbbclhyklvV4Po9EIk8lEwproNKK6BCkqKsLatWsDjs+aNQuzZs1Svebpp5/u7GFFHS1/dsXocHtR3+xGsrX1zOZYwvM8zGazFBrZVtRyKYDAHtUMljugJpzlDmf2M8sJaG92MTObybOU/XdRaiYvtguQO3jbSri1stSeTcqBiAZU9jUGzJmkDBP+xwffx2gkbYdVoWX/2hrW2haCCU15drc8Uqmj2cU6nU6aE8/zit1OqBLqWq0WBoOh3aYfeal1qsRMxCP0WxkDxg7urXi9ZXcVDhyvj81gOgjrjcH+RcpOLm90JKe1FXckBC3bNWk0GphMJkkZyp3zkUSn00lZ4LRDIOIRUhQxwO1RrnodHhGvbzwEj7fr19rR6/VSJdtoOldZ/H97dxSs1wMr581qVPnXMmO7lkj7ScJt+kQQsSA+w226OWpJdcdqmvDDj3UYnp8WgxFFHrmzNRTykNSOIK+0qlafqTWYQ1ntviwklykIttsIp2JuZ9Leyq/ykFrKmCbCgZYwMUCj4ZCWqBQyZr0WWamhi/h1R0wmU6uNl8KFCcBg0UmtXRvsuJrSi3WyHYB2V35lPh4W1uvf94Ig/CFFESMyk5VKgdOIyEju3H4V8QpLFmtLv+1QyKubssQ0efc6NViPB7bKZv/8myExx7N/34pgsLInrKw6K03OFJpayfBwUWsv6/9stUqv8s8GCFQcBOEPKYoY8eD88xSv9//YGKORxA9GoxFmszlkboW8rlEo04nT6VT0vQi202CwwoQmk0nq/yDvA8FgyWWsJ3drzm15DSx5KXPWtIn9aw/yfhVqJdtZZr3/GJnviCk9ueIgRUGoQT6KGJGSoExgoz/Ps7DVfahs8HAEmlwAy00soWBmHLVyHGx3wJ4t91sEU1hspS9XcJEUyOHswvyf5V/oUF45l3wWhBqkKOIEH4Cq0zb0Se/86rFdAZbgF6qta1thLV3T09MlJ7W8fIZOp1OUEtdqtYrdjZqpqbUkO2aukl/HzFnBekZEAnnb2tbuH6xCLkEwyPQUQ/w//GVv7MTpBmdMxhKvmM3miEcXmc1muN1uOBwOyXfh8XjgcDgUOw7mU3A6nXA4HIqMdOa/CKcECSuTLi+ayK5vS22ntsKSBinslugo9BsUQy4aka147fH60Ghzo9Huxn93VeF4TfA2sT0JeTZ4NPHvtSCnLYXwWBlwoGc28GEmP4qs6rrQfjOGjBvcC//ZUSm97pVkRH6fRCxb+x2OVjfDoOMxYXgW9lTU4oJhvTGpJDvE3XoG/sqCOWujKYBYDoU8HyEY/vkK8pBWnU7X7TOx5fkp7LMIdydGxA+kKGJIUW6y4vX+Y3UAgEZ7i4nD4fbgo+1HoeU1+Ofn5ZgwLAs8T5tAOWr29fYWLgwX1pHP6/UGlBZnUUxsx8G6/wFny5YzpeZyuWAymRSKhoWzdpfS3Swk2b/HOCmKrgUpihiSmax0XDPr+A0/G4jPvqvEoLwUfLazCidr7cjJsJKSCJNQzlmPx4OmpqYOl02Xh5TKHcZs9cwa+cgFJIsqkudzyK+Vh/B2l/ahrAe3vHcGOc67HvSNxZhkiw71tpY495xeVgDAgOwkDMhOAgCMLMrAj9XN6JeZELMxdkfkeRjtaaYk7+vt38tb7ryW997W6XRSAiALwQ3laGalQtpbvjwekDvve7KfpqtDiiLG8JqzfzSHKxsC3jcZtBiYkxzFEXVveJ5Hc3MzrFarJMDkfg65cG/N76HX6wMEPVMGTFEwXwYTjhzHwWg0qvo2WLtYVsOJRWNpNBpFUp1a61WW4c1W60xZyccn79vBEu2YiU5tLmr43yMc5HMnuiakKGKMzXk2Kczuiv9ud10djuPQ3NyssJEHM1W11jPc6XQG+BjUssXVBGQwocnKg/iXIXG73VKWOEtEZIpInn0tb+gEnFUAPM9LSoE54/1DgVtTFHLF4t+vg+jekNE7xsg2FOgGVca7FXq9vtU8B4fD0e77y+tQyYU2UwBMeTEHsDwrnB0HlAUK/bO+5VnkoY61NcObwlx7FrSjiDEZKSb8eMomvT7d4EB6Us8sDhiPaLXaVs1QdrtdssGz1bpc8MpX/Ow9eX9s5ifxF9wsmkpeq4mZl5izmyXxMQXBkhOZA5ndUxAEKUNco9HA6XQqIrbCMQvJs8zJId2zoB1FjJk3pUjx+r0vK2I0EkINebHAUD0f5Kt7p9OpMFkx/wGLapI3Q1Jb3cthZjEmpDUajVQMUF7Vlo2ROcj1en3AeHU6ncLkxHwdbfE1sHtQtnfPgr7tGHNuUYbi9RubyrFlz4kYjYYIBhOSJpMprLBVlmjm77Rmwl5+T7nQV3uuXq+H0WhU5Gt0hNbKkxOEP/RbEmMSzIF1jL47dDoGIyHCJZzaU8wB7e9/YM5mg8EgrfrlwprVlurM3hA8z0s9uklREOFAvyVxgNHP3Lv/WH1MxkGEB2u0pIbcCQ0EVpyV7zLUhHSornUs4zwS3fWoDSrRFkhRxAFD/fpkl59oglugUNl4Ry08lO0EQglh1vHOP4oJCB6JJO9Wp9YsKRId8wgiGKQo4oDrpg5QvOY5H8pPUMe7eCdYUT95NJMa8q53/lnhzGfh37WutdW/vFseUyqskx4pDqKjUIxbHDAkT7mjcAhAVho1MOoKGAwG1eZKcuEsL18hfz+Y+SmY34CFwbbmTGdRVWyXwbKoW9vpEEQwaEcRB6j98dpd7eujTESf1vpkBFvRs1Bau90Oh8MRtFw6S8wTBCGob4F1zWP/lz9XFEX07t07wLFOEOESVUWxb98+zJ49GyUlJZg5cyZ2796tet7evXtx7bXXYuTIkbjwwgvx1FNPdfvts//f/qHK+piMg2gfHW2qJIoiBEGAw+GQFIfdbofb7ZYc2GqmKoa8ex5DXnRQLYubIMIlaorC7XZj0aJFmD59Or7++mssXLgQCxYsQHOzsoubw+HAb37zG0yfPh3btm3DmjVr8O677+Ktt96K1lBjgkmv/Cp8vu6tGLsjkezCxwQ7S9Bjx1jLVqZImENc3kVO3otDr9dLhQmBs9Vt5bWk/BEEQWoRSxBAFBXF9u3bIQgC5s+fD51OhxkzZqCwsBAbNmxQnHfixAmUlJTguuuuA8/z6NevH6ZMmYIdO3ZEa6gx4Ry/yKfjpwPt3kTXoLPbtsrrOXm9XklxOJ1OOJ1OhdJgfb/r6+sV5UOYs9t/p86uYTscggCiqCgOHTqEgoICxbH8/HwcOHAg4NjKlSul1263G59//jkGDx4clXHGij7pSuf1+q+OwOakP9SuTFsVButqF8kkOKYUevXqJflDQtWukvs/KBmPYEQt6slut8NoNCqOmUymkNU33W43fve738FkMmHOnDlhP6usrKxdY3Q6ne2+tqMMSFW27qyud2HzV7vRP7PzVqZAbOccK2I156SkJCQlJQV93+FwoKamJmhpcxa9ZDQakZiYKBXoC6ekiDzyiu046uvrYbPZkJCQAFEU0dzcLBUN1Ol0Ujnzrgr9bkeOqCkKs9kc8IvncDiCrrhqampwyy23QKPR4MUXXwxQMqEYNGhQu8ZYVlbW7ms7yiAAj35QhSSLHrPG5KCu2Y0TNj0u6eTxxHLOsSLWc1YLpwVa/AnZ2dkwGo2K6rDBYCYoFi0VylGtVnMqLS0NaWlnTZ69e/fuUFVY+RjioY1rrL/nWNCROYcy70dtb1lQUICKCmVl1PLychQWFgace+jQIfziF79AXl4e1qxZE3IV1p0o6JOEWWNyMKx/CiYMy8QFwzNjPSSiE2jNHMVqPcmd0gzW/U6eh2EwGGA0GmE2m2EymaS2q/KKt+H4Gzqa1c3KqctLqBPdg6gpitGjR0MURaxZswaCIGD9+vXYv38/pk6dqjivoaEBN954Iy655BIsX748rAJs3YUrx/dHXbMbX+w5hXVbj2HFu3uolEc3xWw2t7p69+9w53K5pGzrYIKfVZtlCoIpjBMnTigUiZr/wT9iqq109xD2nkzUFIVer8fq1avx8ccfY9SoUVi1ahVWrlyJ1NRUrFu3DqWlpQCA999/H6dOncLatWtRWloq/bvjjjuiNdSYMWZwJj7YegzrvjqGz/eeQtmxJny2syrWwyI6CVaqIxjMfMMyrSNRjkNettxgMASYiFgnPebwbstzWSXcSDvkidgT1RIeRUVFWLt2bcDxWbNmYdasWQCAefPmYd68edEcVtxgNelQOiANOw6ekY5Vnm4OcQXR1eF5HmazWdVvwXYcaiVAItGvmkVZ+edMyEuAAGed6KGUGgDJr8LOJ7oPpPbjjD9dP1Lx+vVNh3G6of19mYmugVqwBjMv8TwvmZKYEI6kIJabqth95YqJ7SpY7oXT6QzwQTBzGPknuiekKOIMoz5wk9dop3yK7g5rZypHLnDZ6h9AxPtVy30ZzDGuBvOPAGcd3/67D4B8Fd0RUhRxhtpK8Z0vyuF00yqtuxPKri/3LXRm6Km857YaLCSX53lpF8Ec4PKe3qHwer0KpRMObfWXEJGFFEUcwvvpir0VtfjfXuqj3RPwF7KxKuKn1WphNpslk5QceW0pOayXRmu5H4IgSA2Y5MUKWf0p+T95xBeL+lJr+ER0LqQo4pCxQ5X5E063F5kpnZuhTcQH/ooi1tnRzCRlNBoVCoCVBmHCvCNRTkwZCIIg1atiHQCZUpHXtyKiDymKOOT6KUWK16cbXeiflRCj0RDRxF/gxotwZGU91BBFEU6nM2jpETnMhKbVaqXdR7A5sl0Ly1BnZjGtVktRVVGGFEUc0r9PYCb6X9/aFYOREPGA0+mM9RAAtOx2QvlIWERUa+YyJuyZUpS/9m//yrLMmZPdYDBE3JlPtA4pijjlotI+itdf/XASXi81nekJdMRP0dkOX+boDiasWXZ3W8uBaLVaGAwGmEwmqeouyyIHWu8ZTnQupCjilBt+NlDx2u0FHnxlBzU06gGoRRyFU1qDCWmWz9BZMPMRE+j+5jI2DtYnQ60FK3NWE10DUhRxSu9UM/zXUOVVjdSjoofg7w9gBffCJZq+DYPBENSZzXItmIOaKTJ5i1ci/iFFEccU9rEqXrtcLiSYe06RxJ6MmuOYtSgNBsdxUi5DJEp8hAsr76HVaiXHs5qpSN5xT54XQcQ/pCjimD/fMFrxus5Oq6+ehJqwZ6tzNViGNWt5Gk2YOYr5GViWt3/tJ6Yg/HuCx0t0F6EOKYo4Jj3ZFHBsx8HqGIyEiAXBdgVdZSXO87wUrRTKNMWipZhPg/k1SHnED6Qo4px7rj5H8fquZ7fHaCRELAjW5Mi/4mtXgO0qWDlyOSyhjv2T15AK1uNb3sSJynt0LqQo4pyLzs0JOPbulgqVM4nuSihl0VWcwTqdTqqCy5Lt2M/Bdhsul0tygvtHT7ESIKxuFCvx4XQ6OzXiq6dCiqILYDYov6aV73+PZgf9MfQkzGazqoNYHkkUzzsMln8hL5fO/CnMPMWS6+Qlz+U7BFbqHDgb1eVf2oPtRmhnEVlIUXQB3rhvWsCxv71Nmdo9DZMp0GcFnHUIy4vsdTXkikSuLNTOAyBFVwGQ+oezfxqNBqIoIiUlRQrBZeHFXfXziTWUC98FMBu10ACQGxk+33MyVsMhYkiwbnjMJOPxeLpFLSQWagucdd77lzDXaDSS/4KFBrNdh8fjgdVqVYTiAi1KJVgJdSI4tKPoIqxfOj3g2KGq+ugPhIg5wXwWwNndRXeA7RB4nlctGyI3O7Fdg/+OQe4MZ2YppjzkTnMiNKQougg6HQ+dX6OKhY9vwd2rv8LB4w0xGhURK4KZoeThpsxv0VMEoVrfDPn/2c/M8c1KnVCGeOuQouhCvPOniwOOfXPgNFZ98H0MRkPEElZZNRgsKohFAmVlZUmradbroavb6+UVZ9muQx5B1VrSoVw5dPXPorMhRdGFMBm1+P0vhwQc311eS7/oPZC22Np1Op20kmYF+cLpHxHPsD7j8izwcEuX+Hw+aQfCsseJ4NCn08X42aj+MOj8kpUAXPuXj2MzICKmhPJXhEIUxTaXAu8K8DwvdeNjcwuVFd7azoxogRRFF2T90ksCjlU3evDrv34Wg9EQsYaV+25rIUCmLLobGo0GJpMJVVVVUhtXZpZiuwh5aC3baXVHxRkpSFF0UT5eFqgsyk/aMOUPH+Lk6cYYjIiINTqdTlIa4SKvSMsihOQlM5iJSh5m2lZh2p5rIoHX65UUAuvOx0xVer0ePM+D53lpXqyHht1ul/759+3uqdCeq4vC8xp8+uilmPKHDwPeu2755wCAWy4fiFnjCqQ/FhaPTjbZ7g9TFmo5F/54vd6A85iDWL7jkCe1Mf+IPNlNDXnGuMFgiHl+h9wvwfIuQikAFgAgCIKUzMeUBstXYZ9VrOfWmURVWuzbtw+zZ89GSUkJZs6cid27d6ueV1VVhRtuuAGlpaWYMmUKNm/eHM1hdik+ffTSoO+teG8fpt65Hnc887kUNslq4wAtfwRWq7VHr5S6O2yHcfTo0TZdp2aWYrkHTFDKGxAF+x2SRxbFYwiqRqMJ6NUdDP/yICzbm4Ujs2gyVlalO5USiZqicLvdWLRoEaZPn46vv/4aCxcuxIIFC9Dc3Bxw7h133IHi4mJs27YNf/7zn3H77bfj2LFj0Rpql+PTRy9Fbrox6Pu7yxsx9c71mHX/p7hlxRY4XW4pfNJqtYbVZpPo+rTVLKUGUxbMHAOEVgDMUcwEcrzBmi6xelMsG1z+fjjIK9gyc53L5ZLKptvtdkmBMF9IV1IiUTM9bd++HYIgYP78+QCAGTNm4NVXX8WGDRtw1VVXSedVVFRg7969eOGFF6DX6zF27FhMnjwZ//znP3H77bdHa7hdjhfumgIAmHHPh3AF8U/6AFRUO/GLv3yO9CQD7pk9DABworYej/3zexRkJUCnBSC2lA0RfRzyelngEb0oyEpEVpoZiQl6ZFjNsJr14Pmzf/xerw88H3+CgAhErizCMU2p4Z/97XA4Qp7flnBcVqrD5/MpqsuykNZIm3jk9zQYDKrnyDO55WYoeXmQYMj9Pexc/4KH8rH4lyZhStbn8wU0gWKfTWebvaKmKA4dOoSCggLFsfz8fBw4cEBx7PDhw8jKylL8Mufn5wc1UxFK1j/cYop6/4uDWLFuf9DzTje48MrGcuRnWbFlbzXGDsrALy7IAwC8+MkhfHuoDgDwXXntT1dUdWhcmSlGLLh4ADgOeO7fB3GqTr1LW/Q43OoZHFpCj2MB99N/mCzhOMCg1cDt8cEnnj1Ho+Hg84ngOEjHE806pCQYUFPvwFlZJCIl4QSK+ibhv7tOnH0OBxh0GjjdZ4Udr+Hg9YmK+eu1Gni8Pmg0HDzelqNGnQbgIF3LcUBuhgWNNheW3DAC6UlGNDsE3PbMdjQ7PchINKDRLsApnH2WRjZuADAbeDhcXum5SWYtGh0ehLP49r9XC61/z62Rl2nBkzeNkoS2vJyIvxKQZ4OHcoCHOi5XwvKyI0CggpbDcRzS09MlBRNJoqYo7HY7jEalecRkMgWsRGw2m+p5wdo/EupcdsEAXHbBAHg8Xly95FPUNQeWJd95uBY7D7cogvFDeknHe6eYsPdIfUTHM6o4HWmJBunnD746HtH7dwaxNAyIfgMQRSgELHvb6xOl9xmNdgE2Z4t5wyf+pHQAnGl04Ys9J5T3EKFQEpDfU3bM7fnJzOQ9ezRgPCJwrMaGJIsOydYWZ7fFqEVqggFNDg+qGwL7ffsLdrtLKQgb7OGH7wYqichw9JQNtU1upCUaJMHNFIW/c9wfVqTQfwcRzk6kPWi12q6tKMxmc0BjeIfDEWAzDfe8UJSVlbVrjE6ns93XxjMPXZUr/byzog5vbqmFv97YtOsk0pMMEDwi/vdD5Nut7j/WiAlDMwEAB45T+G57CHeHw2sAg5aDS2jZaXBoWe1rNSISTVrUNEYud4LjlEpKp9WgwSZg066TOH9wBr7/sR5Ha2wAAL2Wg+ARQ87Bf1egvkuILmYDj0SzVgoTttvtaGzs+O+wRqMBz/OwWCyS38dgMMDlciEpKUkK3WU+RBat6PP5FD8DkMx6zc3NOHky8pWlo6YoCgoKsGbNGsWx8vJyXH755QHnVVVVwel0SjuL8vJyFBYWhv2sQYMGtWuMZWVl7b62qzBoEHC1LAVDbc7XTh2oeH2qthkNNhfONDlx6owdPKeBVs/h1BkndFoONqcXiRY9LCY9HA433F4fapvc8Hi9MOg0EMEht1cCTjcJ2FV+GjkZZmQm6nHekN74bGcljtfYkJVqRk4vC4bmpcHh9sLmElDf4MC3h89g4jnZqG104cipRjhdHmSmWFDb5EBOphXpCSYU5ybjxGkbTtU7YDHp4fX4UHGyEfVNLhTlJMFqMsDuEuATRThcHtScroPdq0Wzw4ORgzLQLzMRldU2gAMK+iThTKMLqYl69Em1YE/5Gbg8XgzplwqX0GJeOFZtQ0aSHnsq6qHXcxg3KAs7y0+joE8SIAIVJxug5Xk4XB6kJephNerh9Hhh0GogeIG8XgnY8n0l0hNNyM6w4lBlAzQQkWAxoKbBgfOKMlDd4EJOhhWVp21wuAT0SjUjK8WMJruAfcdq4RZEnFOQBg5AdYMDaYkmNNpcONPkwojCdJiMOlSetsFs4CF4fDj64xEMLB6APmlmbNl7ApWnbeibbgagwcDcFHz5/UlU1jQjO8OCgTnJqGt2I9mix5HqZiSatMjJTMDx6makJRpxusGByjM2nD80C16vDzsPnUFmigmNdgH52YnQaTjUNblRVmnDgJw0PPqbMbA5PCjISsCPp5phMvAoO1qLRrsbF5VkYd+xelScbEJuuhVD+iWj0SHgy++r0S/TgnMHpONAZSOa7QJ0Wg2qzjiQYNEhM9EAu9sLn08ERBFaLY8+aWZUnrEh2aLD4RPN4H0C0tMS0Gz34mS9HX3SLDhyqhl1TU70SbNgRFEvnKi147+7KlF+vBa1thZFrOcArwjk9jLiZ+flYuqIvjCbztaRSklJQXZ2duf+obaTU6dOtVuG7dixI/ibYpRwuVzihAkTxBdffFF0u93ihx9+KJaWlopnzpwJOPfnP/+5uHTpUtHlcolbt24VS0pKxP3794f1nG+++abdY/zhhx/afW1XhebcM6A59ww6MudQsjNqYSp6vR6rV6/Gxx9/jFGjRmHVqlVYuXIlUlNTsW7dOpSWlkrnrlixAocPH8bYsWNx3333YcmSJSgqKorWUAmCIAgZUc3MLioqwtq1awOOz5o1C7NmzZJeZ2Vl4bnnnovm0AiCIIggUOA7QRAEERJSFARBEERISFEQBEEQISFFQRAEQYSEFAVBEAQREk4Uu1AJwzAImTRCEARBBGXEiBGqx7udoiAIgiAiC5meCIIgiJCQoiAIgiBCQoqCIAiCCAkpCoIgCCIkpCgIgiCIkJCiIAiCIELSoxTFvn37MHv2bJSUlGDmzJlB+3BXVVXhhhtuQGlpKaZMmYLNmzdHeaSRI9w57927F9deey1GjhyJCy+8EE899VTQvr7xTrhzZgiCgCuvvBIrVqyI0ggjT7hzbm5uxt13341Ro0Zh9OjR+OMf/whBCGyT2xUId87l5eWYN28eRo4ciYkTJ+KZZ56J8kgjz+7duzF27Nig70dchrW7y0UXw+VyiZMmTVI0Tho5cqTY1NQUcO7s2bPFhx9+WHS5XOKXX34plpaWikePHo3BqDtGuHO22+3iuHHjxFdeeUX0eDxiRUWFOHnyZPGNN96I0cjbT1u+Z8YjjzwiDhw4UHzyySejONLI0ZY533LLLeJNN90kNjU1iadPnxavuOIK8ZlnnonBqDtGW+bM5uj1esWKigpx7Nix4qeffhqDUXccn88nvvXWW+KIESPEESNGBD0v0jKsx+wotm/fDkEQMH/+fOh0OsyYMQOFhYXYsGGD4ryKigrs3bsXt956K/R6PcaOHYvJkyfjn//8Z4xG3n7CnfOJEydQUlKC6667DjzPo1+/fpgyZUqXzHIPd86Mbdu24csvv8T48eOjPNLIEe6cq6ursWnTJvzlL3+B1WpFWloann76acycOTNGI28/bfmeKyoqAACiKILjOHAcB4PBEO0hR4Qnn3wSa9euxU033RT0nM6QYT1GURw6dAgFBQWKY/n5+Thw4IDi2OHDh5GVlQWz2aw4b//+/VEZZyQJd875+flYuXKl9NrtduPzzz/H4MGDozLOSBLunAGgoaEB999/P5YvXw6dThetIUaccOdcVlaGrKwsrFu3DhdddBEmTpyI1157DZmZmdEcbkRoy/e8aNEirFixAsOGDcO0adNw6aWXdtmFwZw5c/DOO+9g6NChQc/pDBnWYxSF3W6H0WhUHDOZTHA4HIpjNptN9Tyn09npY4w04c5Zjtvtxu9+9zuYTCbMmTOns4cYcdoy5wceeADXXHNNl2+zG+6c6+vrUVlZiYMHD2LdunV49dVXsWnTpi7ZTbIt3zPHcbjrrrvw3Xff4f3338d//vMfvP3229EaakQJR6l3hgzrMYrCbDbD5XIpjjkcDoXWbct5XYG2zqWmpgbz5s3DmTNn8OKLLwb8snUFwp3zO++8g7q6Olx//fXRHF6nEO6c9Xo9vF4v7r77blgsFuTk5GD+/Pn45JNPojnciBDunPfs2YM1a9Zg3rx5MBgMGDhwIBYsWKDakrm70BkyrMcoioKCAslWySgvL0dhYWHAeVVVVQrtq3ZeVyDcOQMtW/lf/OIXyMvLw5o1a5CUlBStYUaUcOe8fv167N69G+eddx5GjhyJzZs349lnn8VvfvObaA43IoQ75/z8fABAY2OjdMzr9Xb+ADuBcOd88uRJCIKgiODTarXQarVRGWcs6BQZ1m43eBfD5XKJEyZMUERJlJaWimfOnAk49+c//7m4dOlS0eVyiVu3bhVLSkrE/fv3x2DUHSPcOdfX14sXXHCBuGzZshiNNHK05XuWc9NNN3XpqKdw53zllVeKv/3tb0WbzSYeP35cvPjii8Xnn38+BqPuGOHO+fTp0+LIkSPFJ554QhQEQayoqBCnTZsmrlmzJkYjjwxfffVVyKinSMuwHqMoRFEU9+/fL86ZM0csKSkRL730UvHLL78URVEU33//fbGkpEQ6r6qqSlywYIF47rnnihdddJG4fv36WA25w4Qz55deekksKioSzznnHLGkpET6d/vtt8dy6O0m3O9ZTldWFKIY/pzPnDkj3nHHHeLYsWPF0aNHi4888ojo8XhiNewOEe6cv/vuO/Hqq68WR4wYIU6aNElctWqV6PV6YzXsiOCvKDpbhlE/CoIgCCIkPcZHQRAEQbQPUhQEQRBESEhREARBECEhRUEQBEGEhBQFQRAEERJSFARBEERISFEQRBSw2WwoLi7Gtm3bwjq/trYWH3zwgfR67ty5WL58eWcNjyBC0n3z2AmiC/Poo4/CZrNJJcBXrFjRrctOEPEN/eYRRBzinwebnJwcm4EQBMj0RHRjiouL8fbbb+PSSy9FSUkJbrzxRhw7dkx6/z//+Q8uvfRSDBs2DJMnT1aU2xYEAY899hjOP/98jBgxAgsWLEB5ebn0/uTJk/Hqq69Kr48fP47i4mKpH4LD4cDixYsxYsQITJgwAR9//LFibG63G08++SQmT56MYcOG4eqrr8auXbsAtOwe3n33XXz88ccoLi4GoDQ9rVixArfccgsee+wxjBgxAmPGjMHLL7+Mb775BjNnzkRJSQl+9atfob6+Xnre5s2bcdlll2H48OGYMWMG/vWvf0XoUyZ6AqQoiG7NY489hkWLFuGtt94Cx3H41a9+BUEQcPr0adx+++24/vrr8e9//xt33nknnnjiCWzduhVASyexzZs34/HHH8dbb72F/v37Y+7cuWhqagrruQ8++CC++eYbPPvss1ixYgXWrFmjeP+hhx7Cv/71LzzwwAN47733MGDAANxwww2orq7GjTfeiOnTp2PSpEnYsmWL6v0/++wz2O12vPvuu5gzZw6WLVuGP//5z3jggQfw/PPP4/vvv8dLL70EADh48CBuvfVWzJkzBx9++CF++9vfYvny5Vi/fn37P1iiR0GKgujWzJs3D5dccgmKioqwfPlyVFZW4ssvv0R1dTUEQUBWVhays7Pxs5/9DGvWrMGAAQPgdDqxZs0a/OlPf8KoUaNQUFCA++67D1arFe+//36rz2xubsaHH36Ie+65ByNGjMA555yDhx56SHq/sbER77zzDu655x5MnDgRBQUF+NOf/oSsrCy89tprsFgsMBqN0Ov1yMjIUH2GwWDAvffei9zcXMydOxderxfXXXcdRo4ciREjRmDixIk4dOgQAOC5557DzJkzcfXVVyM3NxeXXHIJbrzxRjz//POR+ZCJbg/5KIhuzciRI6Wf09PTkZ2djYMHD2LChAmYOXMmFixYgJycHFx44YW47LLLkJ6ejgMHDsDtdmPBggXgOE663uVyBfRAUKO8vByCIChayQ4dOhQaTcu6rKKiAl6vF6WlpdL7Go0GpaWlOHjwYFjzys7OlpzbrMFU3759pff1ej3q6uoAtOwoDhw4oNhBeDweco4TYUO/KUS3hud5xWufzwee58FxHB577DEsWLAAGzduxObNm/H6669j6dKlkl/ghRdeQFpamuJ6q9Wq+hy1BkByhzTP89JYDAaD6j1EUYTP5wtrXmpCnikitbHNnTu3S7a2JeIDMj0R3ZoffvhB+rmmpgYnTpzAwIEDsW/fPjz88MMYNGgQbr75Zrz99tuYPn061q9fj9zcXGi1WtTW1iIvLw95eXnIzc3FihUrsHv3bgCATqdT+CvkTvL8/HzodDrpXADYv38/BEEAAOTl5UGn0+G7776T3hdFEbt27ZK60Ml3Mh2loKAAP/74ozSXvLw8bN++Ha+//nrEnkF0b2hHQXRrnnnmGeTl5aF3795Yvnw5CgsLMWrUKFRXV2Pt2rVITEzErFmzUF1djZ07d+Kyyy6DxWLB1VdfjSVLlkCn06Ffv35Ys2YNNm7ciFtvvRUAMGzYMLz55puYOHEiBEHAE088IQl3q9WKX/7yl3j44YeRmJgIq9WKBx98UHrfZDLhuuuuw8MPPwyTyYS+ffvitddew7Fjx3DVVVcBaOl7fODAAVRWViI7O7tDn8GNN96Iq666CqtWrcL06dOxb98+LF26FDfddFOH7kv0HEhREN2aq666CkuXLkV1dTXGjRuHZ599FjzPIysrCytWrMATTzyBZ599FlarFZdeeikWLlwIALjzzjuh1Wpx7733orm5GQMHDsTq1auRm5sLALj99tuxePFizJ49G3369MG9996rELz33HMPeJ7HLbfcAo1Gg1tvvRVlZWXS+3fccQc4jsM999wDm82GYcOG4eWXX0a/fv0AAFdccQU+/fRTXHLJJfj000879BkMHToUTz75JJ588kk89dRTyMjIwK9//Wv83//9X4fuS/QcqMMd0W0pLi7GqlWrMGnSpFgPhSC6NOSjIAiCIEJCioIgCIIICZmeCIIgiJDQjoIgCIIICSkKgiAIIiSkKAiCIIiQkKIgCIIgQkKKgiAIgggJKQqCIAgiJP8frNA6NeNwV+wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cm5 - GRIK2')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an array with the colors you want to use\n",
    "colors = [\"whitesmoke\", \"#4374B3\"]\n",
    "# Set your custom color palette\n",
    "customPalette = sns.set_palette(sns.color_palette(colors))\n",
    "i = 0\n",
    "for cluster in list(set(df.ClusterName)):\n",
    "    df['curr'] = df[\"ClusterName\"]==cluster\n",
    "    ax = sns.scatterplot(x=\"pseudotime\", y=\"entropy\", hue=\"curr\", data=df, s=10,linewidth=0, alpha=0.8,\n",
    "                        palette = customPalette, legend=False)\n",
    "    plt.title(cluster)\n",
    "    plt.savefig('pseudotime_entropy_' + str(i) +'.png', bbox_inches='tight')\n",
    "    i+= 1\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
